PROJECT TITLE :

PDA: Semantically Secure Time-Series Data Analytics with Dynamic User Groups - 2018

ABSTRACT:

Third-party analysis on non-public records is changing into increasingly necessary due to the widespread data collection for varied analysis purposes. However, the data in its original kind typically contains sensitive data regarding people, and its publication will severely breach their privacy. In this Project, we have a tendency to present a completely unique Privacy-preserving Data Analytics framework PDA, that permits a third-party aggregator to obliviously conduct many completely different types of polynomial-based mostly analysis on private information records provided by a dynamic sub-cluster of users. Notably, every user desires to keep only O(n) keys to join information analysis among O(2n) totally different teams of users, and any knowledge analysis that is represented by polynomials is supported by our framework. Besides, a real implementation shows the performance of our framework is cherish the peer works who gift ad-hoc solutions for specific data analysis applications. Despite such nice properties of PDA, it is provably secure against a very powerful attacker (chosen-plaintext attack) even within the Dolev-Yao network model where all communication channels are insecure.