A STUDY OF PRIVACY-PRESERVING WHILE SHARING SENSITIVE INFORMATION?

Modern society is increasingly dependent on (and fearful of) massive amounts and availability of electronic information. There are numerous everyday scenarios where sensitive data must be sometimes reluctantly or suspiciously shared between entities without mutual trust. This prompts the need for mechanisms to enable limited (privacy-preserving) information sharing. A typical scenario involves two parties: one seeks information from the other that is either motivated, or compelled to share only the requested information. This paper highlights two main technical challenges: (1) How to enable this type of sharing such that parties learn no information beyond what they are entitled to, and (2) How to do so efficiently, in real-world practical terms. In this paper, it is shown that slicing preserves better data utility than generalization and can be used for membership disclosure protection. Another important advantage of slicing is that it can handle highdimensional data. In this paper, it is shown how slicing can be used for attribute disclosure protection, Membership disclosure protection and develop an efficient algorithm for computing the sliced data that obey the L-diversity requirement.