Privacy preserving dm_ppt
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This document summarizes techniques for privacy-preserving data publishing. It discusses k-anonymity, which anonymizes data by suppressing or generalizing attributes until each record is identical to at least k-1 other records. The document also covers limitations of k-anonymity, including that it does not protect against background knowledge attacks. It proposes addressing these limitations by combining k-anonymity with generalization and slicing techniques.
Privacy preserving dm_ppt
- 1. Sagar MukeshsinghVerma Software Engineer EEWTechnology Nagpur Maharashtra India 1 Privacy Preserving Data Mining
- 2. Privacy preserving data publishing 2 A typical scenario for data collection and publishing is described in Figure above. In the data collection phase, the data publisher collects data from record owners (e.g., Alice and Bob).
- 3. Privacy preserving data publishing In the data publishing phase, the data publisher releases the collected data to a data miner or to the public, called the data recipient, who will then conduct data mining on the published data. In this survey, data mining has a broad sense, not necessarily restricted to pattern mining or model building. For example, a hospital collects data from patients and publishes the patient records to an external medical centre. In this example, the hospital is the data publisher, patients are record owners, and the medical centre is the data recipient. 3
- 4. Related work given in Base Paper 4 The base paper explain about the privacy preservation on publish data by using anonymization technique through record elimination method. First of all explain about type of attack on publish data. There are two kind of major attack on publish data:- 1> Record linkage(Identity discloser attack) 2> Attribute linkage(Attribute discloser attack) Identity discloser attack:- Identity discloser occurs where new information about some individual is revealed, i.e., the released data make it possible to infer the characteristic of an individual more accurately than it would be possible before the data release. Attribute discloser attack:- attribute discloser can occur with or without identity discloser. it has been recognized that even discloser of false attribute information may cause.
- 5. 5 Related work given in Base Paper In this paper the K-Anonymity technique used for preserve the publish data with the comparison with other technique given below. 1> L-Diversity 2> T-Closeness K-anonymity:- The data base said to be k-anonymous where attribute are suppressed or generalized until each row is identical with at least k-1 other row. The data anonymization technique are :- 1> Generalization 2> Bucketization 3> Suppresion
- 6. 6 Limitation/Drawback of k-anonymization technique Some limitation of k-anonymity technique are given below:- 1> It does not hide whether a given individual is in the database. 2> It reveals individuals' sensitive attributes. 3> It does not protect against attacks based on background knowledge. 4> mere knowledge of the k-anonymization algorithm can violate privacy. 5> It cannot be applied to high-dimensional data without complete loss of utility . 6> Special methods are required if a dataset is anonymized and published more than once.
- 7. 7 Literacy survey on paper t-Closeness: Privacy Beyond k-Anonymity and -Diversity The t-closeness Principle:- An equivalence class is said to have t-closeness if the distance between the distribution of a sensitive attribute in this class and the distribution of the attribute in the whole table is no more than a threshold t. A table is said to have t-closeness if all equivalence classes have t-closeness.
- 8. 8 Proposed work with behalf of Base Paper The proposed work on behalf of base paper I want to used:- 1> The concept of generalization 2> Slicing