Privacy preserving delegated access control in public cloud

CERIAS Tech Report 2012-11
Privacy Preserving Delegated Access Control in Public Clouds
by Mohamed Nabeel, Elisa Bertino
Center for Education and Research
Information Assurance and Security
Purdue University, West Lafayette, IN 47907-2086

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Privacy Preserving Delegated Access Control

in Public Clouds
Mohamed Nabeel, Elisa Bertino Fellow, IEEE
Abstract—Current approaches to enforce fine-grained access control on confidential data hosted in the cloud are based on
fine-grained encryption of the data. Under such approaches, data owners are in charge of encrypting the data before uploading
them on the cloud and re-encrypting the data whenever user credentials or authorization policies change. Data owners thus
incur high communication and computation costs. A better approach should delegate the enforcement of fine-grained access
control to the cloud, so to minimize the overhead at the data owners, while assuring data confidentiality from the cloud. We
propose an approach, based on two layers of encryption, that addresses such requirement. Under our approach, the data owner
performs a coarse-grained encryption, whereas the cloud performs a fine-grained encryption on top of the owner encrypted data.
A challenging issue is how to decompose access control policies (ACPs) such that the two layer encryption can be performed. We
show that this problem is NP-complete and propose novel optimization algorithms. We utilize an efficient group key management
scheme that supports expressive ACPs. Our system assures the confidentiality of the data and preserves the privacy of users
from the cloud while delegating most of the access control enforcement to the cloud.
Index Terms—Privacy, Identity, Cloud Computing, Policy Decomposition, Encryption, Access Control
✦
1 INTRODUCTION
Security and privacy represent major concerns in the
adoption of cloud technologies for data storage. An
approach to mitigate these concerns is the use of
encryption. However, whereas encryption assures the
confidentiality of the data against the cloud, the use of
conventional encryption approaches is not sufficient
to support the enforcement of fine-grained organiza
tional access control policies (ACPs). Many organiza
tions have today ACPs regulating which users can
access which data; these ACPs are often expressed
in terms of the properties of the users, referred to as
identity attributes, using access control languages such
as XACML. Such an approach, referred to as attribute-
based access control (ABAC), supports fine-grained
access control which is crucial for high-assurance
data security and privacy. Supporting ABAC over
encrypted data is a critical requirement in order to
utilize cloud storage services for selective data sharing
among different users. Notice that often user identity
attributes encode private information and should thus
be strongly protected from the cloud, very much as
the data themselves.
Approaches based on encryption have been pro
posed for fine-grained access control over encrypted
data [2], [3]. As shown in Figure 1, those approaches
group data items based on ACPs and encrypt each
• A preliminary version of this paper appears in the Proceedings of the
IEEE International Conference on Data Engineering (IRI ’12) [1] as
an invited paper.
• M. Nabeel and E. Bertino are with the Department of Computer
Science, Purdue University, West Lafaytte, IN, 47907.
Email: nabeel@cs.purdue.edu
group with a different symmetric key. Users then
are given only the keys for the data items they are
allowed to access. Extensions to reduce the number of
keys that need to be distributed to the users have been
proposed exploiting hierarchical and other relation
ships among data items. Such approaches however
have several limitations:
Owner Cloud
User
(1) Register
(2) Keys
(3) Selectively encrypt
& upload
(5) Download to re-encrypt
(4) Download &
decrypt
Fig. 1: Traditional approach
• As the data owner does not keep a copy of
the data, whenever the user dynamics or ACPs
change, the data owner needs to download and
decrypt the data, re-encrypt it with the new keys,
and upload the encrypted data. Notice also that
this process must be applied to all the data items
encrypted with the same key. This is inefficient
when the data set to be re-encrypted is large.
• In order to issue the new keys to the users, the
data owner needs to establish private communi
cation channels with the users.
• The privacy of the identity attributes of the users
is not taken into account. Therefore the cloud can
learn sensitive information about the users and
their organization.

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• They are either unable or inefficient in supporting
fine-grained ABAC policies.
Recently proposed approaches based on broadcast
key management schemes [4], [5], [6] address some
of the above limitations. We refer to these approaches
as single layer encryption (SLE) approaches, since, like
previous approaches, they require the data owner to
enforce access control through encryption performed
at the data owner. However, unlike previous ap
proaches, SLE assures the privacy of the users and
supports fine-grained ACPs.
However, while SLE addresses some limitations
of previous approaches, it still requires the data
owner to enforce all the ACPs by fine-grained encryp
tion, both initially and subsequently after users are
added/revoked or the ACPs change. All these encryp
tion activities have to be performed at the owner that
thus incurs high communication and computation
cost. For example, if an ACP changes, the owner must
download from the cloud the data covered by this
ACP, generate a new encryption key, re-encrypt the
downloaded data with the new key, and then upload
the re-encrypted data to the cloud. In this paper, we
propose a new approach to address this shortcoming.
The approach is based on two layers of encryption ap
plied to each data item uploaded to the cloud. Under
this approach, referred to as two layer encryption (TLE),
the data owner performs a coarse grained encryption
over the data in order to assure the confidentiality of
the data from the cloud. Then the cloud performs fine
grained encryption over the encrypted data provided
by the data owner based on the ACPs provided by the
data owner. It should be noted that the idea of two
layer encryption is not new. However, the way we
perform coarse and fine grained encryption is novel
and provides a better solution than existing solutions
based on two layers of encryption [7]. We elaborate in
details on the differences between our approach and
existing solutions in the related work section.
A challenging issue in the TLE approach is how
to decompose the ACPs so that fine-grained ABAC
enforcement can be delegated to the cloud while at the
same time the privacy of the identity attributes of the
users and confidentiality of the data are assured. In
order to delegate as much access control enforcement
as possible to the cloud, one needs to decompose the
ACPs such that the data owner manages minimum
number of attribute conditions in those ACPs that
assures the confidentiality of data from the cloud.
Each ACP should be decomposed to two sub ACPs
such that the conjunction of the two sub ACPs result
in the original ACP. The two layer encryption should
be performed such that the data owner first encrypts
the data based on one set of sub ACPs and the cloud
re-encrypts the encrypted data using the other set of
ACPs. The two encryptions together enforce the ACP
as users should perform two decryptions to access the
data. For example, if the ACP is (C1 ∧ C2) ∨ (C1 ∧ C3),
the ACP can be decomposed as two sub ACPs C1 and
C2 ∨ C3. Notice that the decomposition is consistent;
that is, (C1 ∧ C2) ∨ (C1 ∧ C3) = C1 ∧ (C2 ∨ C3). The
data owner enforces the former by encrypting the
data for the users satisfying the former and the cloud
enforces the latter by re-encrypting the data owner
encrypted data for the users satisfying the latter. Since
the cloud does not handle C1, it cannot decrypt owner
encrypted data and thus confidentiality is preserved.
Notice that users should satisfy the original ACP to
access the data by performing two decryptions. In
this paper, we show that the problem of decomposing
ACPs such that the data owner manages the minimum
number of attribute conditions while at the same
time assuring the confidentiality of the data in the
cloud is NP-complete. We propose two optimization
algorithms to find the near optimal set of attribute
conditions and decompose each ACP into two sub
ACPs.
The TLE approach has many advantages. When
the policy or user dynamics changes, only the outer
layer of the encryption needs to be updated. Since the
outer layer encryption is performed at the cloud, no
data transmission is required between the data owner
and the cloud. Further, both the data owner and the
cloud service utilize a broadcast key management
scheme [8] whereby the actual keys do not need to
be distributed to the users. Instead, users are given
one or more secrets which allow them to derive the
actual symmetric keys for decrypting the data.
The rest of the paper is organized as follows. Sec
tion 2 describes in detail the underlying building
blocks used to construct our system. An overview
of the TLE approach is given in Section 3. Section 4
provides a detailed treatment of the policy decom
position for the purpose of two layer encryption.
Section 5 gives a detailed description of the TLE
approach. Section 6 reports experimental results for
policy decomposition algorithms and the SLE vs. the
TLE approaches. We briefly analyze the trade-offs, the
security and the privacy of the overall systems in
Section 7. Section 8 discusses the related work and
compare them with our approach. Finally, Section 9
concludes the paper providing future directions.
2 BUILDING BLOCKS
In this section we first introduce broadcast encryp
tion schemes [9], [10] and oblivious commitment
based envelope protocols [11]. We present an ab
stract view of the main algorithms of those protocols
and then describe how we use them to build our
privacy-preserving attribute based group key man-
agement (PP AB-GKM) scheme [8]. We then present
an overview of the SLE approach [4], [5], [6] which is
used as the base model for comparison with the TLE
approach proposed in this paper.

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2.1 Broadcast Encryption
Broadcast encryption (BE) [9] was introduced to solve
the problem of how to efficiently encrypt a message
and broadcast it to a subset of the users in a system.
The subset of users can change dynamically. In the
broadcast encryption literature, these users are called
privileged and the non-authorized users revoked. We
denote the set of users by U, the set of revoked
users R. The set of privileged users is thus UR. We
set N = |U| and r = |R|. While all users can get
the encrypted message, only the privileged users can
decrypt it. The most simplest broadcast encryption
scheme simply consists of encrypting a message for
each privileged user separately and the broadcasting
all the encrypted messages. Obviously, this scheme is
very inefficient as the message length is prohibitively
large (O(N −r)). Better broadcast encryption schemes
aim to reduce the following parameters:
• The processing time at the server to encrypt the
message for the privileged users.
• The processing time at privileged users to decrypt
messages.
• The broadcast message size.
• The storage size at both the server and privileged
users.
There are two approaches to broadcast encryption.
The first approach assumes that users are stateful
meaning that the keys given to users can be updated
when a new user is added or an existing user is
revoked. The second approach assumes that users are
stateless meaning that the keys given to users cannot
be updated and can only be discarded. We consider
only the latter approach since in the outsourced sce
narios the keys initially given to users are difficult to
update and, therefore, remain unchanged.
We use an algorithm based on subset-cover algo
rithm that supports broadcast encryption with state
less users. The algorithm builds a binary tree and
assigns users to the leaf nodes and thus results in a
predefined user grouping. Each such group is called
a subset. A user can be a member of several subsets.
The cover, denoted by C, is defined as the set of subsets
that contains all the privileged users, that is, users in
U/R. The subsets in the cover are disjoint and hence
each privileged user belongs to only one subset.
Definition 1 (Broadcast Encryption): A subset-cover
based Broadcast Encryption (BE) scheme consists
of the algorithms: Setup, GetSecKeys, GetCover,
Broadcast, KeyDer and Decrypt. Each of these
algorithms are described below:
Setup(ℓ, N): The server constructs a binary tree
Λ where there are at least N leaf nodes. Each node
in Λ is either assigned a unique key whose length
is decided by the security parameter ℓ, or can
computationally derive a unique key. The user ui,
i = 1, 2, · · · , N, is assigned the ith
leaf node.
GetSecKeys(ui): The server gives all the keys
assigned to ui in Λ.
GetCover(UR): Given the privileged user set
UR, the server outputs the cover C.
Broadcast(M, C): The server generates a session
key K and encrypts the message M with K and
encrypts K with each key in the cover C.
KeyDer(ui, C): The user ui identifies its subset in the
cover C, outputs the key that decrypts the session key.
Decrypt(C, K): It decrypts the encrypted message C
with the key K, to output the message M.
Having defined the algorithms, we give a high-level
description of the basic subset-cover technique. In the
basic scheme, N users are organized as the leaves
of a balanced binary tree of height log N. A unique
secret is assigned to each vertex in the tree. Each user
is given log N secrets that correspond to the vertices
along the path from its leaf node to the root node.
In order to provide forward secrecy when a single
user is revoked, the updated tree is described by log N
subtrees formed after removing all the vertices along
the path from the user leaf node to the root node. To
rekey, the server uses the log N secrets corresponding
to the roots of these subtrees. Many improved subset-
cover based broadcast encryption algorithms have
been proposed. In this work, we consider the complete
subtree algorithm [10]. The complete subtree algorithm
improves the basic technique for simultaneously re
voking r users and describing the privileged users
using r log (N/r) subsets. Each user stores log N keys.
2.2 Oblivious Commitment Based Envelope Pro
tocols
The Oblivious Commitment Based Envelope (OCBE)
protocols, proposed by Li and Li [11], provide a
mechanism to obliviously deliver a message to the
users who satisfy certain conditions. There are three
entities in these protocols, a server Svr, a user Usr,
and a trusted third party, called the identity provider
(IdP). IdP issues to Usr identity tokens, expressed
as Pedersen commitments [12], corresponding to the
identity attributes of Usr.
Definition 2 (OCBE): An OCBE protocol consists of
the algorithms: Setup, GenCom and GetData. Each
of these algorithms are described below:
Setup(ℓ): The IdP runs a Pedersen commitment
setup protocol to generate the system parameters,
a finite cyclic group G of large prime order p, two
generators g and h of G. The size of p is dependent
on the security parameter ℓ.

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GenCom(x): A Usr wants to commit to the value x. It
submits x to the IdP. The IdP computes the Pedersen
x
hr
commitment c = g where r is randomly chosen
from Fp. The IdP digitally signs c and sends r, c and
the signature of c to the Usr.
GetData(c, cond, d): The Usr sends the signed
commitment c and indicates the Svr’s condition
cond that it wants to satisfy. cond has the format
“name predicate value” where the predicate can be
>, ≥, <, ≤ or =. For example, cond “age ≥ 18” and c
are for the attribute age. After an interactive session,
the Svr encrypts the data d and sends the encrypted
data, called envelope, to the Usr. The Usr can decrypt
and access the data only if it satisfies the condition.
As mentioned above, the OCBE protocols always
guarantee the following properties:
• The Svr does not learn the identity attributes of
the users.
• A Usr can open the envelope only if its committed
attribute value satisfies the condition.
• A Usr cannot submit fake commitments in order
to satisfy a condition as the commitments are
signed by the IdP.
2.3 Privacy Preserving Attribute Based Group
Key Management
The privacy preserving attribute based group key
management (PP AB-GKM) scheme uses the BE
scheme introduced in Section 2.1 and the OCBE proto
cols introduced in Section 2.2. Such scheme combines
the previous work on AB-GKM scheme [8], [13] and
privacy preservation in Broadcast Group Key Man
agement (BGKM) [4], [6].
The BGKM schemes are a special type of GKM
scheme where the rekey operation is performed with a
single broadcast without requiring the use of private
communication channels. Unlike conventional GKM
schemes, the BGKM schemes do not give users the
private keys. Instead users are given a secret which
is combined with public information to obtain the
actual private keys. Such schemes have the advantage
of requiring a private communication only once for
the initial secret sharing. The subsequent rekeying
operations are performed using one broadcast mes
sage. Further, in such schemes achieving forward and
backward security requires only to change the public
information and does not affect the secret shares given
to existing users. In general, a BGKM scheme consists
of the following five algorithms: Setup, SecGen, Key-
Gen, KeyDer, and ReKey. Our overall construction
is based on the AB-GKM scheme [8], [13] which is
an expressive construct of the ACV-BGKM (Access
Control Vector BGKM) scheme [4], [6].
Before we present details of the PP AB-GKM pro
tocol, we first define certain terms that are useful to
describe the protocol. Attribute conditions and access
control policies are formally defined as follows.
Definition 3 (Attribute Condition): An attribute con
dition C is an expression of the form: “nameattr op l”,
where nameattr is the name of an identity attribute
attr, op is a comparison operator such as =, <, >,
≤, ≥, �=, and l is a value that can be assumed by the
attribute attr.
Definition 4 (Access control policy): An access con
trol policy ACP is a tuple (s, o) where: o denotes a
set of data items {D1, . . . , Dt}; and s is a monotonic
expression 1
over a set of attribute conditions that
must be satisfied by a Usr to have access to o.
We denote the set of all attribute conditions as ACB
and the set of all ACPs as ACPB. Example 1 shows
an example ACP.
Example 1: The ACP
((“yos ≥ 5′′
∧ “role = nurse′′
) ∨ “role = doctor′′
,
{physical exam, treatment plan})
states that a Usr, either playing the role of doctor or
playing the role nurse with yos, years of service, no
less than 5, has access to the data items “physical
exam” and “treatment plan”.
Before providing a detailed description of the PP
AB-GKM scheme, we present the intuition and ab
stract algorithms. A separate BGKM instance for each
attribute condition is constructed. The ACP is embed
ded in an access structure T . T is a tree in which
the internal nodes represent threshold gates and the
leaves represent BGKM instances for the attributes. T
can represent any monotonic policy. The goal of the
access tree is to allow the derivation of the group key
for only the users whose attributes satisfy the access
structure T . Figure 2 shows the access tree for the
ACP in Example 1.
role = doctor
OR
AND
yos >= 5 role = nurse
Fig. 2: An example access tree
A high-level description of the access tree is as
follows. Each threshold gate in the tree is described by
its child nodes and a threshold value. The threshold
value tx of a node x specifies the number of child
nodes that should be satisfied in order to satisfy the
node. Each threshold gate is modeled as a Shamir
secret sharing polynomial [14] whose degree equals
to one less than the threshold value. The root of the
tree contains the group key and all the intermediate
values are derived in a top-down fashion. A user who
1. Monotonic expressions are Boolean formulas that contain only
conjunction and disjunction connectives, but no negation.

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satisfies the access tree derives the group key in a
bottom-up fashion.
Due to space constraints, we only provide the
abstract algorithms of the PP AB-GKM scheme.
As a convention, we use the format <protocol
name>::<algorithm> to refer to the constructs
introduced earlier or elsewhere. Specifically,
BE::<algorithm>, OCBE::<algorithm> and ACV
BGKM::<algorithm> [4] to refer to algorithms of BE,
OCBE and ACV-BGKM protocols respectively.
Definition 5 (PP AB-GKM): The PP AB-GKM
scheme consists of five algorithms: Setup, SecGen,
KeyGen, KeyDer and ReKey. An abstract description
of these algorithms are given below.
Setup(ℓ, N, Na): It taking the security parameter
ℓ, the maximum group size N, and the number
of attribute conditions Na as input, initializes the
system. It invokes BE:Setup(ℓ, N), OCBE::Setup(ℓ)
and ACV-BGKM::Setup(ℓ, N) algorithms.
SecGen(γ): The secret generation algorithm gives a
Usrj, 1 ≤ j ≤ N a set of secrets for each commitment
comi ∈ γ, 1 ≤ i ≤ m. It invokes BE::GetSecGen and
OCBE::GetData algorithms.
KeyGen(ACP): The key generation algorithm
takes the access control policy ACP as the input
and outputs a symmetric key K, a set of public
information tuples PI and an access tree T . It invokes
BE::GetCover() and ACV-BGKM::KeyGen algorithms.
KeyDer(β, PI, T ): Given the set of identity attributes
β, the set of public information tuples PI and the
access tree T , the key derivation algorithm outputs
the symmetric K only if the identity attributes in β
satisfy the access structure T . It invokes BE:KeyDer
and ACV-BGKM::KeyDer algorithms.
ReKey(ACP):

The rekey algorithm is similar to the KeyGen

algorithm. It is executed whenever the dynamics in

the system change.

2.4 Single Layer Encryption Approach
The SLE scheme [6] consists of the four entities,
Owner, Usr, IdP and Cloud. They play the following
roles:
• Owner, the data owner defines ACPs, and up
loads encrypted data to the Cloud, the cloud
storage service.
• The Cloud hosts the encrypted data of the Owner.
• IdP, the identity provider, a trusted third party,
issues identity tokens to users based on the at
tribute attributes users have. An identity token
is a signed Pedersen commitment that binds the
identity attribute value to a Usr while hides it
from others. There can be one or more certified
IdPs. We assume that all IdPs issue identity to
kens in the same format.
• Usr, the user, uses one or more identity tokens to
gain access to the encrypted data hosted in the
Cloud.
Owner Cloud
User
(1) Register
identity tokens
(2) Secrets
& upload
(5) Download to re-encrypt
(4) Download &
decrypt
User IdP
(1) Identity attribute
(2) Identity token
Fig. 3: Single Layer Encryption approach
As shown in Figure 3, the SLE approach follows
the conventional data outsourcing scenario where the
Owner enforces all ACPs through selective encryption
and uploads encrypted data to the untrusted Cloud.
The system goes through five different phases. We
give an overview of the five phases below:
Identity token issuance: IdPs issue identity tokens to
Usrs based on their identity attributes.
Identity token registration: Usrs register all their
identity tokens to obtain secrets in order to later
decrypt the data that they are allowed to access.
Data encryption and uploading: Based on the secrets
issued and the ACPs, the Owner encrypts the data
using the keys generated using the AB-GKM::KeyGen
algorithm and uploads to the Cloud.
Data downloading and decryption: Usrs download
encrypted data from the Cloud and decrypt using the
key derived from the AB-GKM::KeyDer algorithm.
Encryption evolution management: Over time, either
access control polices or user credentials may change.
Further, already encrypted data may go through fre
quent updates. In such situations, it may be required
to re-encrypt already encrypted data. The Owner
alone is responsible to perform such re-encryptions.
The Owner downloads all affected data from the
Cloud, decrypts them and then follows the data en
cryption and upload step.
3 OVERVIEW
We now give an overview of our solution to the prob
lem of delegated access control to outsourced data in
the cloud. A detailed description is provided in Sec
tion 5. Like the SLE system described in Section 2.4,
the TLE system consists of the four entities, Owner,
Usr, IdP and Cloud. However, unlike the SLE ap
proach, the Owner and the Cloud collectively enforce
ACPs by performing two encryptions on each data

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item. This two layer enforcement allows one to reduce
the load on the Owner and delegates as much access
control enforcement duties as possible to the Cloud.
Specifically, it provides a better way to handle data
updates, user dynamics, and policy changes. Figure 4
shows the system diagram of the TLE approach. The
system goes through one additional phase compared
to the SLE approach. We give an overview of the six
phases below:
User IdP
(1) Identity attribute
(2) Identity token
(5) Re-encrypt to(1) Decompose
enforce policiespolicies

& upload docs &

Owner Cloudmodified policies
(2) Register
(2) Register identity tokens
identity tokens
(3) Secrets
(6) Download &
decrypt twiceUser
(3) Secrets
Fig. 4: Two Layer Encryption approach
Identity token issuance: IdPs issue identity tokens to
Usrs based on their identity attributes.
Policy decomposition: The Owner decomposes each
ACP into at most two sub ACPs such that the Owner
enforces the minimum number of attributes to assure
confidentiality of data from the Cloud. It is important
to make sure that the decomposed ACPs are consistent
so that the sub ACPs together enforce the original
ACPs. The Owner enforces the confidentiality related
sub ACPs and the Cloud enforces the remaining sub
ACPs.
Identity token registration: Usrs register their iden
tity tokens in order to obtain secrets to decrypt the
data that they are allowed to access. Usrs register
only those identity tokens related to the Owner’s sub
ACPs and register the remaining identity tokens with
the Cloud in a privacy preserving manner. It should
be noted that the Cloud does not learn the identity
attributes of Usrs during this phase.
Data encryption and uploading: The Owner first
encrypts the data based on the Owner’s sub ACPs
in order to hide the content from the Cloud and
then uploads them along with the public information
generated by the AB-GKM::KeyGen algorithm and
the remaining sub ACPs to the Cloud. The Cloud in
turn encrypts the data based on the keys generated
using its own AB-GKM::KeyGen algorithm. Note that
the AB-GKM::KeyGen at the Cloud takes the secrets
issued to Usrs and the sub ACPs given by the Owner
into consideration to generate keys.
Data downloading and decryption: Usrs download
encrypted data from the Cloud and decrypt the data
using the derived keys. Usrs decrypt twice to first
remove the encryption layer added by the Cloud and
then by the Owner. As access control is enforced
through encryption, Usrs can decrypt only those data
for which they have valid secrets.
Encryption evolution management: Over time, either
ACPs or user credentials may change. Further, already
encrypted data may go through frequent updates. In
such situations, data already encrypted must be re-
encrypted with a new key. As the Cloud performs
the access control enforcing encryption, it simply re-
encrypts the affected data without the intervention of
the Owner.
4 POLICY DECOMPOSITION
Recall that in the SLE approach, the Owner incurs
a high communication and computation overhead
since it has to manage all the authorizations when
user dynamics or ACPs change. If the access control
related encryption is somehow delegated to the Cloud,
the Owner can be freed from the responsibility of
managing authorizations through re-encryption and
the overall performance would thus improve. Since
the Cloud is not trusted for the confidentiality of the
outsourced data, the Owner has to initially encrypt
the data and upload the encrypted data to the cloud.
Therefore, in order for the Cloud to allow to enforce
authorization policies through encryption and avoid
re-encryption by the Owner, the data may have to be
encrypted again to have two encryption layers. We
call the two encryption layers as inner encryption layer
(IEL) and outer encryption later (OEL). IEL assures the
confidentiality of the data with respect to the Cloud
and is generated by the Owner. The OEL is for fine-
grained authorization for controlling accesses to the
data by the users and is generated by the Cloud.
An important issue in the TLE approach is how
to distribute the encryptions between the Owner and
the Cloud. There are two possible extremes. The first
approach is for the Owner to encrypt all data items
using a single symmetric key and let the Cloud per
form the complete access control related encryption.
The second approach is for the Owner and the Cloud
to perform the complete access control related encryp
tion twice. The first approach has the least overhead
for the Owner, but it has the highest information
exposure risk due to collusions between Usrs and
the Cloud. Further, IEL updates require re-encrypting
all data items. The second approach has the least
information exposure risk due to collusions, but it
has the highest overhead on the Owner as the Owner
has to perform the same task initially as in the SLE
approach and, further, needs to manage all identity
attributes. An alternative solution is based on de
composing ACPs so that the information exposure
risk and key management overhead are balanced. The
problem is then how to decompose the ACPs such
that the Owner has to manage the minimum number

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of attributes while delegating as much access control
enforcement as possible to the Cloud without allowing
it to decrypt the data. In what follow we propose such
an approach to decompose and we also show that the
policy decomposition problem is hard.
4.1 Policy Cover
We define the policy cover problem as the the opti
mization problem of finding the minimum number
of attribute conditions that “covers” all the ACPs in
the ACPB. We say that a set of attribute conditions
covers the ACPB if in order to satisfy any ACP in the
ACPB, it is necessary that at least one of the attribute
conditions in the set is satisfied. We call such a set of
attribute conditions as the attribute condition cover. For
example, if ACPB consists of the three simple ACPs
{C1 ∧ C2, C2 ∧ C3, C4}, the minimum set of attributes
that covers ACPB is {C2, C4}. C2 should be satisfied
in order to satisfy the ACPs C1 ∧ C2 and C2 ∧ C3.
Notice that satisfying C2 is not sufficient to satisfy the
ACPs. The set is minimum since the set obtained by
removing either C2 or C4 does not satisfy the cover
relationship. We define the related decision problem
as follows.
Definition 6 (POLICY-COVER): Determine whether
ACPB has a cover of k attribute conditions.
The following theorem states that this problem is
NP-complete.
Theorem 1: The POLICY-COVER problem is NP-
complete.
Proof: We first show that POLICY-COVER ∈ NP.
Suppose that we are given a set of ACPs ACPB which
contains the attribute condition set AC, and integer k.
For simplicity, we assume that each ACP is a conjunc
tion of attribute conditions. However, the proof can
be trivially extended to ACPs having any monotonic
Boolean expression over attribute conditions. The cer
tificate we choose has a cover of attribute conditions
′
AC ⊂ AC. The verification algorithm affirms that
′
|AC | = k, and then it checks, for each policy in the
′
ACPB, that at least one attribute condition in AC is in
the policy. This verification can be performed trivially
in polynomial time. Hence, POLICY-DECOM is NP.
Now we prove that the POLICY-COVER problem is
NP-hard by showing that the vertex cover problem,
which is NP-Complete, is polynomial time reducible
to the POLICY-COVER problem. Given an undirected
graph G = (V, E) and an integer k, we construct a set
of ACPs ACPB that has a cover set of size k if and
only if G has a vertex cover of size k.
′
Suppose G has a vertex cover V ⊂ V with |V ′
| = k.
We construct a set of ACPs ACPB that has a cover
of k attribute conditions as follows. For each vertex
vi ∈ V , we assign an attribute condition Ci. For each
vertex vj ∈ V ′
, we construct an access control policy
by obtaining the conjunction of attribute conditions as
follows.
• Start with the attribute condition Cj as the ACP
Pj
• For each edge (vj, vr), add Cr to the ACP as a
conjunctive literal (For example, if the edges are
(vj, va), (vj, vb) and (vj, vc), we get Pj = Cj ∧Ca ∧
Cb ∧ Cc)
At the end of the construction we have a set
of distinct access control policies ACPB with size
k. We construct the attribute condition set AC =
{C1, C2, · · · , Ck} such that Ci corresponds to each
vertex in V ′
. In order to satisfy all access control poli
cies, the attribute conditions in AC must be satisfied.
Hence, AC is an attribute condition cover of size k for
the ACPs ACPB.
Conversely, suppose that ACPB has an attribute
condition cover of size k. We construct G such that
each attribute condition corresponds to a vertex in G
and an edge between vi and vj if they appear in the
same access control policy. Let this vertex set be V1.
Then we add the remaining vertices to G correspond
ing to other attribute conditions in the access control
policies and add the edges similarly. Since the access
control policies are distinct there will be at least one
edge (vi, u) for each vertex vi in attribute condition
cover such that u �∈ V1. Hence G has a vertex cover of
size V1 = k.
Since the POLICY-COVER problem is NP-complete,
one cannot find a polynomial time algorithm for
finding the minimum attribute condition cover. In
the following section we present two approximation
algorithms for the problem.
The APPROX-POLICY-COVER1 algorithm 2 takes
as input the set of ACPs ACPB and returns a set of
attribute conditions whose size is guaranteed to be
no more than twice the size of an optimal attribute
condition cover. APPROX-POLICY-COVER1 utilizes
the GEN-GRAPH algorithm 1 to first represent ACPB
as a graph.
Algorithm 1 GEN-GRAPH
1: C = φ
2: for Each ACPi ∈ ACPB, i = 1 to Np do
3: ACP′
← Convert ACPi to DNFi
4: for Each conjunctive term c of ACP′
doi
5: Add c to C
6: end for
7: end for
8: //Represent the conditions as a graph
9: G = (E, V ), E = φ, V = φ
10: for Each conjunctive term ci ∈ C, i = 1 to Nc do
11: Create vertex v, if v �∈ V , for each AC in ci
12: Add an edge ei between vi and each vertex
already added for ci
13: end for
14: Return G

8
We give a high-level overview of the GEN-GRAPH
algorithm 1. It takes the ACPB as the input and con
verts each ACP into DNF (disjunctive normal form).
The unique conjunctive terms are added to the set C.
For each attribute condition in each conjunctive term
in C, it creates a new vertex in G and adds edges
between the vertices corresponding to the same con
junctive term. Depending on the ACPs, the algorithm
may create a graph G with multiple disconnected
subgraphs.
Algorithm 2 APPROX-POLICY-COVER1
1: G = GEN-GRAPH(ACPB)
2: ACC = φ
3: for Each disconnected subgraph Gi = (Vi, Ei) of
G do
4: if |Vi| == 1 then
5: Add ACi corresponding to the vertex to ACC
6: else
7: while Ei �= φ do
8: Select a random edge (u, v) of Ei
9: Add the attribute conditions ACu and ACv
corresponding to {u, v} to ACC.
10: Remove from Ei every edge incident on
either u or v
11: end while
12: end if
13: end for
Once an edge is considered, all its incident edges are
removed from Gi. The algorithm continues until all
edges are removed from each Gi. The running time
of the algorithm is O(V + E) using adjacency lists
to represent G. It can be shown that the APPROX
POLICY-COVER1 algorithm is a polynomial-time 2
approximation algorithm.
We now present the idea behind our second ap
proximation algorithm, APPROX-POLICY-COVER2,
which uses a heuristic to select the attribute con
ditions. This algorithm is similar to the APPROX
POLICY-COVER1 algorithm 2 except that instead of
randomly selecting the edges to be included in the
cover, it selects the vertex of highest degree and
removes all of its incident edges.
Example 2: A hospital (Owner) supports ﬁne-
grained access control on electronic health records
(EHRs) and makes these records available to
hospital employees (Usrs) through a public cloud

(Cloud). Typical hospital employees includes Usrs

playing different roles such as receptionist (rec),

cashier (cas), doctor (doc), nurse (nur), pharmacist

(pha), and system administrator (sys). An EHR

document consists of data items including BillingInfo

(BI), ContactInfo (CI), MedicationReport (MR),

PhysicalExam (PE), LabReports (LR), Treatment Plan

(TP) and so on. In accordance with regulations such

as health insurance portability and accountability act

(HIPAA), the hospital policies specify which users

can access which data item(s). In our example system,

there are four attributes, role (rec, cas, doc, nur, pha,

sys), insurance plan, denoted as ip, (ACME, MedA,

MedB, MedC), type (assistant, junior, senior) and year

of service, denoted as yos, (integer). The following is

the re-arranged set of ACPs of the hospital such that

each data item has a unique ACP.

(“role = rec” ∨ (“role = nur” ∧ “type ≥ junior”), CI)

(“role = cas” ∨ “role = pha”, BI)

(“role = doc” ∧ “ip = 2-out-4”, CR)

((“role = doc” ∧ “ip = 2-out-4”) ∨ “role = pha”, TR)

((“role = doc” ∧ “ip = 2-out-4”) ∨ (“role = nur” ∧ “yos ≥ 5”) ∨

“role = pha”, MR)

((“role = nur” ∧ “type ≥ junior”) ∨ (“role = dat” ∧ “type ≥

junior”) ∨ (“role = doc” ∧ “yos ≥ 2”), LR)

((“role = nur” ∧ “type = senior”) ∨ (“role = dat” ∧ “yos ≥ 4”),

PE)

14: Return ACC
As shown in the APPROX-POLICY-COVER1 algo-
rithm 2, it takes the ACPB as the input and outputs
a near-optimal attribute condition cover ACC. First
the algorithm converts the ACPB to a graph G as
shown in the GEN-GRAPH algorithm 1. Then for
each disconnected subgraph Gi of G, it ﬁnds the
near optimal attribute condition cover and add to
the ACC. The attribute condition to be added is re-
lated at random by selecting a random edge in Gi.
role
=
cas role
=
nur
type
=
senior
>=
=
role
type
junior
Type
>=
junior
role
pha
role ip role
= = =
doc 2-out-4 dat yos
>=
5
=
rec
yos
>=
2
yos
>=
4
Fig. 5: The example graph
Figure 5 shows the graph generated by the GEN
GRAPH algorithm for our running example. Notice
that there are 5 disconnected graphs. Assume that
APPROX-POLICY-COVER2 algorithm is used to con
struct the AC cover. As mentioned in the approxi
mation algorithm, single vertex graphs are trivially
included in the AC cover. The remaining attribute
conditions are selected using the greedy heuristic.
That gives us the AC cover ACC = { “role = rec”, “role
= cas”, “role = pha”, “role = doc”, “role = nur”, “role =
dat”}.
4.2 Policy Decomposition
The Owner manages only those attribute conditions in
ACC. The Cloud handles the remaining set of attribute

9
conditions, ACB/ACC. The Owner re-writes its ACPs
such that they cover ACC. In other words, the Owner
enforces the parts of the ACPs related to the ACs in
ACC and Cloud enforces the remaining parts of the
policies along with some ACs in ACC. The POLICY
DECOMPOSITION algorithm 3 shows how the ACPs
are decomposed into two sub ACPs based on the
attribute conditions in ACC.
Algorithm 3 POLICY-DECOMPOSITION
1: ACPBOwner = φ
2: ACPBCloud = φ
3: for Each ACPi in ACPB do
4: Convert ACPi to DNF
5: ACPi(owner) = φ
6: ACPi(cloud) = φ
7: if Only one conjunctive term then
8: Decompose the conjunctive term c into c1 and
c2 such that ACs in c1 ∈ ACC, ACs in c2 �∈
ACC and c = c1 ∧ c2
9: ACPi(owner) = c1
10: ACPi(cloud) = c2
11: else if At most one term has more than one AC
then
12: for Each single AC term c of ACP′
doi
13: ACPi(owner) ∨= c
14: ACPi(cloud) ∨= c
15: end for
16: Decompose the multi AC term c into c1 and c2
such that ACs in c1 ∈ ACC, ACs in c2 �∈ ACC
and c = c1 ∧ c2
17: ACPi(owner) ∨= c1
18: ACPi(cloud) ∨= c2
19: else
20: for Each conjunctive term c of ACP′
doi
21: Decompose c into c1 and c2 such that ACs
in c1 ∈ ACC, ACs in c2 �∈ ACC and c =
c1 ∧ c2
22: ACPi(owner) ∨= c1
23: end for
24: ACPi(cloud) = ACP′
i
25: end if
26: Add ACPi(owner) to ACPBOwner
27: Add ACPi(cloud) to ACPBCloud
28: end for
29: Return ACPBOwner and ACPBCloud
Algorithm 3 takes the ACPB and ACC as input
and produces the two sets of ACPs ACPBOwner and
ACPBCloud that are to be enforced at the Owner and
the Cloud respectively. It first converts each policy into
DNF and decompose each conjunctive term into two
conjunctive terms such that one conjunctive term has
only those ACs in ACC and the other term may or may
not have the ACs in ACC. It can be easily shown that
the policy decomposition is consistent. That is, the
conjunction of corresponding sub ACPs in ACPBOwner
and ACPBCloud respectively produces an original ACP
in ACPB.
Example 3: For our example ACPs, the Owner han
dles the following sub ACPs.
(“role = rec” ∨ “role = nur” , CI)
(“role = doc”, CR)
(“role = doc” ∨ “role = pha”, TR)
(“role = doc” ∨ “role = nur” ∨ “role = pha”, MR)
(“role = nur” ∨ “role = dat” ∨ “role = doc”, LR)
(“role = nur” ∨ “role = dat”, PE)
As shown in Algorithm 3, the Owner re-writes the
ACPs that the Cloud should enforce such that the
conjunction of the two decomposed sub ACPs yields
an original ACP. In our example, the sub ACPs that
the Cloud enforces look like follows.
(“role = rec” ∨ “type ≥ junior”, CI)
(“ip = 2-out-4”, CR)
(“ip = 2-out-4” ∨ “role = pha”, TR)
((“role = doc” ∧ “ip = 2-out-4”) ∨ (“role = nur” ∧ “yos ≥ 5”) ∨
“role = pha”, MR)
((“role = nur” ∧ “type ≥ junior”) ∨ (“role = dat” ∧ “type ≥
junior”) ∨ (“role = doc” ∧ “yos ≥ 2”), LR)
((“role = nur” ∧ “type = senior”) ∨ (“role = dat” ∧ “yos ≥ 4”),
PE)
5 TWO LAYER ENCRYPTION APPROACH
In this section, we provide a detailed description of
the six phases of the TLE approach introduced in
Section 3. The system consists of the four entities,
Owner, Usr, IdP and Cloud. Let the maximum number
of users in the system be N, the current number
of users be n (< N), and the number of attribute
conditions Na.
5.1 Identity token issuance
IdPs are trusted third parties that issue identity tokens
to Usrs based on their identity attributes. It should be
noted that IdPs need not be online after they issue
identity tokens. An identity token, denoted by IT
has the format { nym, id-tag, c, σ }, where nym is a
pseudonym uniquely identifying a Usr in the system,
id-tag is the name of the identity attribute, c is the
Pedersen commitment for the identity attribute value
x and σ is the IdP’s digital signature on nym, id-tag
and c.
5.2 Policy decomposition
Using the policy decomposition algorithm 3, the
Owner decomposes each ACP into two sub ACPs
such that the Owner enforces the minimum number
of attributes to assure confidentiality of data from
the Cloud. The algorithm produces two sets of sub
ACPs, ACPBOwner and ACPBCloud. The Owner enforces
the confidentiality related sub ACPs in ACPBOwner
and the Cloud enforces the remaining sub ACPs in
ACPBCloud.

10
5.3 Identity token registration
Usrs register their IT s to obtain secrets in order to
later decrypt the data they are allowed to access. Usrs
register their IT s related to the attribute conditions
in ACC with the Owner, and the rest of the identity
tokens related to the attribute conditions in ACB/ACC
with the Cloud using the AB-GKM::SecGen algorithm.
When Usrs register with the Owner, the Owner
issues them two sets of secrets for the attribute con
ditions in ACC that are also present in the sub ACPs
in ACPBCloud. The Owner keeps one set and gives the
other set to the Cloud. Two different sets are used in
order to prevent the Cloud from decrypting the Owner
encrypted data.
5.4 Data encryption and upload
The Owner encrypts the data based on the sub ACPs
in ACPBOwner and uploads them along with the cor
responding public information tuples to the Cloud.
The Cloud in turn encrypts the data again based on
the sub ACPs in ACPBCloud. Both parties execute AB
GKM::KeyGen algorithm individually to first generate
the symmetric key, the public information tuple PI
and access tree T for each sub ACP. We now give a
detailed description of the encryption process.
The Owner arranges the sub ACPs such that each
data item has a unique ACP. Note that the same policy
may be applicable to multiple data items. Assume that
the set of data items D = {d1, d2, · · · , dm} and the set
of sub ACPs ACPBOwner = {ACP1, ACP2, · · · , ACPn}.
The Owner assigns a unique symmetric key, called
an ILE key, KILE
for each sub ACPi ∈ ACPBOwner,i
encrypts all related data with that key and executes
the AB-GKM::KeyGen to generate the public PIi and
Ti. The Owner uploads those encrypted data (id,
EKILE (di), i) along with the indexed public infori
mation tuples (i, PIi, Ti), where i = 1, 2, · · · , n,
to the Cloud. The Cloud handles the key man
agement and encryption based access control for
the ACPs in ACPBCloud. For each sub ACPj ∈
ACPBCloud, the Cloud assigns a unique symmetric
key Kj
OLE
, called an OLE key, encrypts each affected
data item EKILE (di) and produces the tuple (id,i
EKOLE (EKILE (di)), i, j), where i and j gives the indexj i
of the public information generated by the Owner and
the Cloud respectively.
5.5 Data downloading and decryption
Usrs download encrypted data from the Cloud and
decrypt twice to access the data. First, the Cloud
generated public information tuple is used to derive
the OLE key and then the Owner generated public in
formation tuple is used to derive the ILE key using the
AB-GKM::KeyDer algorithm. These two keys allow a
Usr to decrypt a data item only if the Usr satisfies the
original ACP applied to the data item.
For example, in order to access a data item di, Usrs
download the encrypted data item EKOLE (EKILE (di))j i
and the corresponding two public information tuples
PIi and PIj. PIj is used to derive the key of the outer
layer encryption KOLE
and PIi used to derive thej
key of the inner layer encryption KILE
. Once thosei
two keys are derived, two decryption operations are
performed to access the data item.
5.6 Encryption evolution management
After the initial encryption is performed, affected data
items need to be re-encrypted with a new symmetric
key if credentials are added/removed or ACPs are
modified. Unlike the SLE approach, when credentials
are added or revoked or ACPs are modified, the
Owner does not have to involve. The Cloud generates
a new symmetric key and re-encrypts the affected
data items. The Cloud follows the following condi
tions in order to decide if re-encryption is required.
1) For any ACP, the new group of Usrs is a strict
superset of the old group of Usrs, and backward
secrecy is enforced.
2) For any ACP, the new group of Usrs is a strict
subset of the old group of Usrs, and forward
secrecy is enforced for the already encrypted
data items.
6 EXPERIMENTAL RESULTS
In this section we first present experimental results
concerning the policy decomposition algorithms. We
then present an experimental comparison between the
SLE and TLE approaches.
The experiments were performed on a machine
running GNU/Linux kernel version 2.6.32 with an
Intel@R CoreTM
2 Duo CPU T9300 2.50GHz and 4
Gbytes memory. Only one processor was used for
computation. Our prototype system is implemented
in C/C++. We use V. Shoup’s NTL library [15] version
5.4.2 for finite field arithmetic, and SHA-1 and AES
256 implementations of OpenSSL [16] version 1.0.0d
for cryptographic hashing and incremental encryp
tion. We use boolstuff library [17] version 0.1.13 to
convert policies into DNF. Adjacency list representa
tion is used to construct policy graphs used in the two
approximation algorithms for finding a near optimal
attribute condition cover.
We utilized the AB-GKM scheme with the subset
cover optimization. We used the complete subset al
gorithm introduced by Naor et. al. [10] as the subset
cover. We assumed that 5% of attribute credentials
are revoked for the AB-GKM related experiments. All
finite field arithmetic operations in our scheme are
performed in an 512-bit prime field.
For our experiments, we selected the total number
of attribute conditions and the number of attribute
conditions per policy based on past case studies [18],

65
70
75
80
85
90
95
700
750
800
850
900
950
1000
11
100 500 80 120
Random
Greedy
460
Random
Greedy
70
440
480
100
80
420
60
400
380
50360
340
320
Time(ms)
Time(ms)
40 60
60 300
2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20
DNF + Graph
Cover
Decompose
100 500 1000
DNF + Graph
Cover
Decompose
100 500 1000
CoversizeCoversize
CoversizeCoversize
Num. of ACs per policy Num. of ACs per policy 30
40
(a) 100 attributes (b) 500 attributes 20
20
10
1450
Random
Greedy
2
600
650
4 6 8 10 12 14 16
Num. of ACs per policy
(c) 1000 attributes
18 20 2
900
950
1000
4 6 8 10 12 14 16
Num. of ACs per policy
(d) 1500 attributes
18
Random
Greedy
20
1400
1350 0 0
1300
Num. of ACs Num. of ACs1250
1200
1150
1100
1050
(a) Random Cover (b) Greedy Cover
Fig. 6: Size of ACCs for different number of ACs
[19]. According to the case studies, the number of
attribute conditions varies from 50 for a web based
conference management system to 1300 for a major
European bank. These real systems have upto about
20 attribute conditions per policy. We set the total
attribute condition count between 100-1500 and the
the attribute conditions per policy count between 2-20.
We generate random Boolean expressions consisting
of conjunctions and disjunctions as policies. Each
term in the Boolean expression represents a attribute
condition.
Figure 6 shows the size of the attribute condition
cover, that is, the number of attribute conditions the
data owner enforces, for systems having 100, 500,
1000 and 1500 attribute conditions as the number
of attribute conditions per policy is increased. In
all experiments, the greedy policy cover algorithm
performs better. As the number of attribute conditions
per policy increases, the size of the attribute condition
cover also increases. This is due to the fact that as the
number of attribute conditions per policy increases,
Fig. 7: Time break down for decomposing policies
graph, whereas the former algorithm simply picks a
pair of unvisited vertices at random. Consistent with
the worst-cast running times, the“DNF + Graph” and
“Decompose” components demonstrate near linear
running time, and ‘the ‘Cover” component shows a
non-linear running time.
Figure 8 reports the average time spent to execute
the AB-GKM::KeyGen with SLE and TLE approaches
for different group sizes. We set the number of at
tribute conditions to 1000 and the maximum number
of attribute conditions per policy to 5. We utilize the
greedy algorithm to find the attribute condition cover.
As seen in the diagram, the running time at the Owner
in the SLE approach is higher since the Owner has
to enforce all the attribute conditions. Since the TLE
approach divides the enforcement cost between the
Owner and the Cloud, the running time at the Owner
is lower compared to the SLE approach. The running
time at the Cloud in the TLE approach is higher
than that at the Owner since the Cloud performs fine
grained encryption whereas the Owner only performs
coarse grained encryption. As shown in Figure 9, a
similar pattern is observed in the AB-GKM::KeyDer
as well.
the number of distinct disjunctive terms in the DNF
increases.
Figure 7 shows the break down of the running
time for the complete policy decomposition process.
In this experiment, the number of attribute condition
is set to {100, 500, 1000} and the maximum number
of attribute conditions per policy is set to 5. The total
execution time is divided into the execution times of
three different components of our scheme. The“DNF
+ Graph” time refers to the time required to convert
0
1
2
3
4
5
6
7
Time(inseconds)
SLE Owner
TLE Owner
TLE Cloud
100 200 300 400 500 600 700 800 900 1000
Group Size
Fig. 8: Average time to generate keys for the two
the policies to DNF and construct a in-memory graph
of policies using an adjacency list. The “Cover” time
refers to the time required to to find the optimal cover
and the “Decompose” time refers to time required to
to create the updated policies for the data owner and
the cloud based on the cover. As can be seen from the
graphs, most of the time is spent on finding a near op
timal attribute condition cover. It should be noted that
the random approximation algorithm runs faster than
the greedy algorithm. One reason for this behavior is
that each time the latter algorithm selects a vertex it
iterates through all the unvisited vertices in the policy
approaches
7 ANALYSIS
In this section, we first compare the SLE and the TLE
approaches, and then give a high level analysis of the
security and the privacy of both approaches.
7.1 SLE vs. TLE
Recall that in the SLE approach, the Owner enforces
all ACPs by fine-grained encryption. If the system

12
0
5
10
15
20
25
30
35
40
45
100 200 300 400 500 600 700 800 900 1000
Time(inmilliseconds)
SLE Owner
TLE Owner
TLE Cloud
Group Size
Fig. 9: Average time to derive keys for the two ap
proaches
dynamics change, the Owner updates the keys and
encryptions. The Cloud merely acts as a storage repos
itory. Such an approach has the advantage of hiding
the ACPs from the Cloud. Further, since the Owner
performs all access control related encryptions, a Usr
colluding with the Cloud is unable to access any data
item that is not allowed to access. . However, the SLE
approach incurs high overhead. Since the Owner has
to perform all re-encryptions when user dynamics or
policies change, the Owner incurs a high overhead
in communication and computation. Further, it is
unable to perform optimizations such as delayed AB-
GKM::ReKey or re-encryption as the Owner has to
download, decrypt, re-encrypt and re-upload the data,
which could considerably increase the response time
if such optimizations are to be performed.
The TLE approach reduces the overhead incurred
by the Owner during the initial encryption as well
as subsequent re-encryptions. In this approach, the
Owner handles only the minimal set of attribute con
ditions and most of the key management tasks are
performed by the Cloud. Further, when identity at
tributes are added or removed, or the Owner updates
the Cloud’s ACPs, the Owner does not have to re-
encrypt the data as the Cloud performs the necessary
re-encryptions to enforce the ACPs. Therefore, the TLE
approach reduces the communication and computa
tion overhead at the Owner. Additionally, the Cloud
has the opportunity to perform delayed encryption
during certain dynamic scenarios as the Cloud itself
manages the OEL keys and encryptions. However, the
improvements in the performance comes at the cost
of security and privacy. In this approach, the Cloud
learns some information about the ACPs.
7.2 Security and Privacy
The SLE approach correctly enforces the ACPs
through encryption. In the SLE approach, the Owner
itself performs the attribute based encryption based
on ACPs. The AB-GKM scheme makes sure that only
those Usrs who satisfy the ACPs can derive the en
cryption keys. Therefore, only the authorized Usrs are
able to access the data.
The TLE approach correctly enforces the ACPs
through two encryptions. Each ACP is decomposed
into two ACPs such that the conjunction of them is
equivalent to the original ACP. The Owner enforces
one part of the decomposed ACPs through attribute
based encryption. The Cloud enforces the counterparts
of the decomposed ACPs through another attribute
based encryption. Usr can access a data item only
if it can decrypt both encryptions. As the AB-GKM
scheme makes sure that only those Usrs who satisfy
these decomposed policies can derive the correspond
ing keys, a Usr can access a data item by decrypting
twice only if it satisfies the two parts of the decom
posed ACPs, that is, the original ACPs.
In both approaches, the privacy of the identity
attributes of Usrs is assured. Recall that the AB-
GKM::SecGen algorithm issues secrets to users based
on the identity tokens which hide the identity at
tributes. Further, at the end of the algorithm neither
the Owner nor the Cloud knows if a Usr satisfies
a given attribute condition. Therefore, neither the
Owner nor the Cloud learns the identity attributes
of Usrs. Note that the privacy does not weaken the
security as the AB-GKM::SecGen algorithm makes
sure that Usrs can access the issued secrets only if their
identity attributes satisfy the attribute conditions.
8 RELATED WORK
Fine-grained Access Control: Fine-grained access
control (FGAC) allows one to enforce selective access
to the content based on expressive policy specifi
cations. Research in FGAC can be categorized into
two dissemination models: push-based and pull-based
models. Our work focuses on the pull-based model.
In the push-based approaches [2], [3] subdocu
ments are encrypted with different keys, which are
provided to users at the registration phase. The en
crypted subdocuments are then broadcasted to all
users. However, such approaches require that all [2] or
some [3] keys be distributed in advance during user
registration phase. This requirement makes it difficult
to assure forward and backward key secrecy when
user groups are dynamic or the ACPs change. Further,
the rekey process is not transparent, thus shifting
the burden of acquiring new keys on users. Shang
et al. [4] proposes approach to solve such problem.
It lays the foundation to make rekey transparent to
users and protect the privacy of the users who access
the content. However, it does not support expressive
access control policies as in our approach and also it
is not directly applicable to pull based approaches.
Under the pull-based model, the content publisher
is required to be online in order to provide access
to the content. Recent research efforts [20], [21], [5],
[22] have proposed approaches to construct privacy
preserving access control systems using a third-party
storage service. In such approaches, the data owner

13
has to enforce the ACPs and the privacy of the users
from the content publisher is not protected. Further,
in some approaches, multiple encryptions of the same
document are required which is inefficient.
A major drawback of all the above approaches
is that they do not consider the management of
encrypted data hosted in a third party when users
are added or removed from the system or when
the ACPs/subdocuments are updated. All the ap
proaches require the data owner to handle encryption.
Di Vimercati et al. [7] first identifies this problem
and proposes an initial solution. While their solution
improves over existing solutions, such solution does
not support expressive attribute based policies and
does not protect the privacy of the users.
Attribute Based Encryption: The concept of attribute-
based encryption (ABE) has been introduced by Sahai
and Waters [23]. The initial ABE system is limited
only to threshold policies in which there are at least
k out of n attributes common between the attributes
used to encrypt the plaintext and the attributes users
possess. Pirretti et al. [24] gave an implementation
of such a threshold ABE system using a variant of
the Sahai-Waters Large Universe construction [23].
Since this initial threshold scheme, a few variants
have been introduced to provide more expressive ABE
systems. Goyal et al. [25] introduced the idea of key
policy ABE (KP-ABE) systems and Bethencourt et
al. [26] introduced the idea of ciphertext-policy ABE
(CP-ABE) systems. Even though these constructs are
expressive and provably secure, they are not suitable
for group management and especially in supporting
forward security when a user leaves the group (i.e.
attribute revocation) and in providing backward se
curity when a new user joins the group. Some of the
above schemes suggest using an expiration attribute
along with other attributes. However, such a solution
is not suitable for a dynamic group where joins and
departures are frequent.
Proxy Re-Encryption: In a proxy re-encryption (PRE)
scheme [27] one party A delegates its decryption
rights to another party B via a third party called
a “proxy.” More specifically, the proxy transforms a
ciphertext computed under party A’s public key into
a different ciphertext which can be decrypted by party
B with B’s private key. In such a scheme neither
the proxy nor party B alone can obtain the plaintext.
Recently Liang et al. [28] has extended the traditional
PRE to attribute based systems and independently
Chu et al. [29] has extended the traditional PRE to
support conditions where a proxy can re-encrypt only
if the condition specified by A is satisfied. These
improved PRE techniques alone or combined with
ABE schemes [30] could be utilized to implement
delegated access control in the cloud. However, they
do not protect the identity attributes of the users who
access the system and are difficult to manage.
9 CONCLUSIONS
Current approaches to enforce ACPs on outsourced
data using selective encryption require organizations
to manage all keys and encryptions and upload
the encrypted data to the remote storage. Such ap
proaches incur high communication and computa
tion cost to manage keys and encryptions when
ever user credentials or organizational authorization
policies/data change. In this paper, we proposed a
two layer encryption based approach to solve this
problem by delegating as much of the access con
trol enforcement responsibilities as possible to the
Cloud while minimizing the information exposure
risks due to colluding Usrs and Cloud. A key problem
in this regard is how to decompose ACPs so that
the Owner has to handle a minimum number of
attribute conditions while hiding the content from
the Cloud. We showed that the policy decomposition
problem is NP-Complete and provided approxima
tion algorithms. Based on the decomposed ACPs, we
proposed a novel approach to privacy preserving fine
grained delegated access control to data in public
clouds. Our approach is based on a privacy preserving
attribute based key management scheme that protects
the privacy of users while enforcing attribute based
ACPs. As the experimental results show, decomposing
the ACPs and utilizing the two layer of encryption
reduce the overhead at the Owner. As future work, we
plan to investigate the alternative choices for the TLE
approach further. We also plan to further reduce the
computational cost by exploiting partial relationships
among ACPs.
ACKNOWLEDGMENTS
The work reported in this paper has been partially
supported by the MURI award FA9550-08-1-0265 from
the Air Force Office of Scientific Research.
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Mohamed Nabeel is a PhD candidate at the department of computer
science, Purdue university. He is also a member of the Center
for Education and Research in Information Assurance and Secu
rity (CERIAS), IEEE and ACM. His research interests are in data
privacy, distributed system security and applied cryptography. His
PhD thesis topic is “Privacy Preserving Access Control for Third-
Party Data Management Systems”. His research adviser is prof.
Elisa Bertino. He has published in the areas of privacy preserving
content dissemination and group key management. He received the
Fulbright fellowship in 2006, Purdue Cyper Center research grant in
2010 and Purdue research foundation grant in 2011.
Elisa Bertino is Professor of Computer Science at Purdue Univer
sity, and serves as research director of the Center for Education
and Research in Information Assurance and Security (CERIAS) and
Interim Director of Cyber Center (Discovery Park). Previously, she
was a faculty member and department head at the Department of
Computer Science and Communication of the University of Milan.
Her main research interests include security, privacy, digital identity
management systems, database systems, distributed systems, and
multimedia systems. She is currently serving as chair of the ACM
SIGSAC and as a member of the editorial board of the following
international journals: IEEE Security & Privacy, IEEE Transactions
on Service Computing, ACM Transactions on Web. She also served
as editor in chief of the VLDB Journal and editorial board mem
ber of ACM TISSEC and IEEE TDSC. She co-authored the book
”Identity Management - Concepts, Technologies, and Systems”. She
is a fellow of the IEEE and a fellow of the ACM. She received
the 2002 IEEE Computer Society Technical Achievement Award
for outstanding contributions to database systems and database
security and advanced data management systems and the 2005
IEEE Computer Society Tsutomu Kanai Award for pioneering and
innovative research contributions to secure distributed systems.

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