Adaptive Privacy of Sequential Data Releases Under Collusion
It addresses the practical problem of privacy-preserving sequential data sharing with colluding receivers, which is a known bottleneck in privacy research.
The paper formulates a privacy-utility trade-off for sequential data releases to multiple potentially colluding parties, and develops an adaptive algorithm that reduces cumulative privacy leakage under collusion without sacrificing utility. Numerical experiments on real data demonstrate the effectiveness of the approach.
The fundamental trade-off between privacy and utility remains an active area of research. Our contribution is motivated by two observations. First, privacy mechanisms developed for one-time data release cannot straightforwardly be extended to sequential releases. Second, practical databases are likely to be useful to multiple distinct parties. Furthermore, we can not rule out the possibility of data sharing between parties. With utility in mind, we formulate a new privacy-utility trade-off problem to adaptively tackle sequential data requests made by different, potentially colluding entities. We consider both expected distortion and mutual information as measures to quantify utility, and use mutual information to measure privacy. We assume an attack model whereby illicit data sharing, which we call collusion, can occur between data receivers. We develop an adaptive algorithm for data releases that makes use of a Blahut-Arimoto-style algorithm. We show that the resulting data releases are optimal when expected distortion quantifies utility, and locally optimal when mutual information quantifies utility. Numerical experiments on real data demonstrate that the proposed adaptive algorithm can exploit previously released information to reduce cumulative leakage under collusion without sacrificing much, if any utility. Finally, we discuss how our findings may extend to applications in machine learning.