Data Privacy for a \rho -Recoverable Function

• Published in: IEEE transactions on information theory Vol. 65; no. 6; pp. 3470 - 3488
• Main Authors: Nageswaran, Ajaykrishnan, Narayan, Prakash
• Format: Journal Article
• Language: English
• Published: IEEE 01.06.2019
• Subjects: Chernoff radius; Differential privacy; Distortion; Estimation; function computation; Indexes; predicate privacy; Privacy; recoverability; Upper bound
• ISSN: 0018-9448; 1557-9654
• DOI: 10.1109/TIT.2019.2894147

A user's data is represented by a finite-valued random variable. Given a function of the data, a querier is required to recover, with at least a prescribed probability, the value of the function based on a query response provided by the user. The user devises the query response, subject to the recoverability requirement, so as to maximize privacy of the data from the querier. Privacy is measured by the probability of error incurred by the querier in estimating the data from the query response. We analyze single and multiple independent query responses, with each response satisfying the recoverability requirement, which provide maximum privacy to the user. In the former setting, we also consider privacy for a predicate of the user's data. Achievability schemes with explicit randomization mechanisms for query responses are given and their privacy compared with converse upper bounds.