problem. The main aim of the privacy preservation is protecting the sensitive information in data while extracting knowledge from large amount of data. There are many techniques are use in privacy preservation like k-anonymity, l-diversity, t-closeness, blocking based method and cryptography techniques.
Aug 07, 2005 PRIVACY-PRESERVING DATA MINING: MODELS AND … 2. k-Anonymity 105 3. Algorithms for Enforcing k-Anonymity 108 4. k-Anonymity Threats from Data Mining 115 4.1 Association Rules 115 4.2 Classification Mining 116 5. k-Anonymity in Data Mining 118 6. Anonymize-and-Mine 120 7. Mine-and-Anonymize 123 7.1 Enforcing k-Anonymity on Association Rules 124 7.2 Enforcing k-Anonymity on Decision Trees (PDF) A Study on k-anonymity, l-diversity, and t-closeness This paper provides a discussion on several anonymity techniques designed for preserving the privacy of microdata. This research aims to highlight three of the prominent anonymization techniques ℓ-Diversity: Privacy Beyondk-Anonymity
A Review of Privacy Preservation Technique
PPDP-MLT: K−ANONYMITY PRIVACY PRESERVATION FOR PUBLISHING SEARCH ENGINE LOGS B.Lavanya1, K. Rajani Devi2 1M.Tech (IT), Nalanda institute of engineering and technology (NIET), k. The privacy concern of a user is that all or some of the information in P(U) may be captured by some other people in the world. The concern may be less if Enhancing Privacy Preservation Using Hybrid Approach Of K
Social Networks |authorSTREAM
These include K-anonymity, classification, clustering, association rule, distributed privacy preservation, L-diverse, randomization, taxonomy tree, condensation, and cryptographic (Sachan et al. 2013). The PPDM methods protect the data by changing them to mask or erase the original sensitive one to be concealed. Subspace k-anonymity algorithm for location-privacy In this paper, a k-anonymity algorithm based on locality-sensitive hashing is proposed to solve the problem of location-privacy preservation in the subspace. In the proposed algorithm, higher efficiency and higher quality of service are achieved by applying a bottom-up grid-search method. Social Networks |authorSTREAM The need for anonymising the network while publishing has been discussed in [4] A model for anonymising is ‘k-anonymity’, proposed in [2] An implementation of k-anonymity is proposed in [1] using minimum DFS code in [3] An isomorphism algorithm used for checking the similarity of neighborhoods which is proposed in [5] has been studied.