PDA: Semantically Secure Time-Series Data Analytics with Dynamic Subgroups

TL;DR

The paper introduces PDA, a privacy-preserving framework enabling secure polynomial-based data analysis on dynamic user groups, with efficient key management and strong security guarantees even in insecure networks.

Contribution

PDA is a novel framework supporting secure, polynomial-based data analysis on dynamic groups with minimal key management and provable security in hostile environments.

Findings

Supports polynomial-based analysis on private data.

Efficient key management for dynamic groups.

Provably secure against chosen-plaintext attacks.

Abstract

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.