Eplq efficient privacy preserving location-based query over outsourced encrypted data

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EPLQ: Efficient Privacy-Preserving Location-based
Query over Outsourced Encrypted Data
Abstract:
With the pervasiveness of smart phones, locationbased services
(LBS) have received considerable attention and become more
popular and vital recently. However, the use of LBS also poses a
potential threat to user’s location privacy. In this paper, aiming
at spatial range query, a popular LBS providing information
about POIs (Points Of Interest) within a given distance, we
present an efficient and privacy-preserving location based query
solution, called EPLQ. Specifically, to achieve
privacypreserving spatial range query, we propose the first
predicateonly encryption scheme for inner product range, which
can be used to detect whether a position is within a given
circular area in a privacy-preserving way. To reduce query
latency, we further design a privacy-preserving tree index
structure in EPLQ. Detailed security analysis confirms the
security properties of EPLQ. In addition, extensive experiments
are conducted, and the results demonstrate that EPLQ is very
efficient in privacypreserving spatial range query over
outsourced encrypted data. In particular, for a mobile LBS user

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using an Android phone, around 0.9 second is needed to
generate a query; and it also only requires a commodity
workstation, which plays the role of the cloud in our
experiments, a few seconds to search POIs.
Introduction:
A few decades ago, location-based services (LBS) were used in
military only. Today, thanks to advances in information and
communication technologies, more kinds of LBS have appeared,
and they are very useful for not only organizations but also
individuals. Let’s take the spatial range query, one kind of LBS
that we will focus on in this paper, as an example. Spatial range
query is a widely used LBS, which allows a user to find POIs
(Point Of Interests) within a given distance to his/her location,
i.e., the query point. As illustrated in Fig. 1, with this kind of
LBS, a user could obtain the records of all restaurants within
walking distance (say 500 meters). Then the user can go through

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these records to find a desirable restaurant considering price and
reviews
Existing system:
In Existing System some sensitive location data of organization
users may involve trade secret or national security. Protecting
the privacy of user location in LBS has attracted considerable
interest. However significant challenges still remain in the
design of privacypreserving LBS, and new challenges arise
particularly due to data outsourcing. In recent years, there is a
growing trend of outsourcing data including LBS data because
of its financial and operational benefits significant challenges
still remain in the design of privacypreserving LBS, and new
challenges arise particularly due to data outsourcing. In recent
years, there is a growing trend of outsourcing data including
LBS data because of its financial and operational benefits

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Disadvantages:
 Querying encrypted LBS data without privacy breach is a
big challenge, and we need to protect not only the user
locations from the LBS provider and cloud, but also LBS
data from the cloud.
 The cryptographic or privacy-enhancing techniques used to
realize privacy-preserving query usually result in high
computational cost and/or storage cost at user side.
Proposed System :
In proposed System we implemented EPLQ, an efficient privacy
preserving spatial range query solution for smart phones, which
preserves the privacy of user location, and achieves
confidentiality of LBS data. To realize EPLQ, we have designed
a novel predicate-only encryption scheme for inner product
range named IPRE and a novel privacy-preserving index tree
named ˆ ss-tree. EPLQ’s efficacy has been evaluated with
theoretical analysis and experiments, and detailed analysis
shows its security against known-sample attacks and ciphertext-

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only attacks. Our techniques have potential usages in other kinds
of privacy-preserving queries. If the query can be performed
through comparing inner products to a given range, the proposed
IPRE and ˆ ss-tree may be applied to realize privacy-preserving
query. Two potential usages are privacypreserving similarity
query and long spatial range query. In the future, we will design
solutions for these scenarios and identify more usages.
Advantages:
 Efficiency
 Accuracy
 Security
Architecture:

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Hardware Requirement:
 System : Pentium IV 2.4 GHz.
 Hard Disk : 40 GB.
 Floppy Drive : 44 Mb.
 Monitor : 15 VGA Colour.
 Mouse : Logitech
 Ram : 512 Mb.
 MOBILE : ANDROID
Software Requirement:
 Operating system : Windows 7.
 Coding Language : Java 1.7
 Tool Kit : Android 2.3 ABOVE
 IDE : Eclipse

Eplq efficient privacy preserving location-based query over outsourced encrypted data

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