  Third-party analysis on private records is becoming increasingly important
due to the widespread data collection for various analysis purposes. However,
the data in its original form often contains sensitive information about
individuals, and its publication will severely breach their privacy. In this
paper, we present a novel Privacy-preserving Data Analytics framework PDA,
which allows a third-party aggregator to obliviously conduct many different
types of polynomial-based analysis on private data records provided by a
dynamic sub-group of users. Notably, every user needs to keep only O(n) keys to
join data analysis among O(2^n) different groups of users, and any data
analysis that is represented by polynomials is supported by our framework.
Besides, a real implementation shows the performance of our framework is
comparable to the peer works who present 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 in the
Dolev-Yao network model where all communication channels are insecure.
