Noiseless Privacy

TL;DR

This paper introduces noiseless privacy, a non-stochastic privacy concept ensuring limited output variability to protect individual data, with formal guarantees, composition properties, and practical quantization-based mechanisms demonstrated on energy and transport datasets.

Contribution

It defines noiseless privacy as a non-stochastic alternative to differential privacy, providing formal guarantees, composition theorems, and practical mechanisms like quantization.

Findings

Output variability is bounded by the privacy budget.

Mechanisms satisfy composition and post-processing invariance.

Quantization can achieve noiseless privacy with appropriate levels.

Abstract

In this paper, we define noiseless privacy, as a non-stochastic rival to differential privacy, requiring that the outputs of a mechanism (i.e., function composition of a privacy-preserving mapping and a query) can attain only a few values while varying the data of an individual (the logarithm of the number of the distinct values is bounded by the privacy budget). Therefore, the output of the mechanism is not fully informative of the data of the individuals in the dataset. We prove several guarantees for noiselessly-private mechanisms. The information content of the output about the data of an individual, even if an adversary knows all the other entries of the private dataset, is bounded by the privacy budget. The zero-error capacity of memory-less channels using noiselessly private mechanisms for transmission is upper bounded by the privacy budget. The performance of a non-stochastic hypothesis-testing adversary is bounded again by the privacy budget. Finally, assuming that an adversary has access to a stochastic prior on the dataset, we prove that the estimation error of the adversary for individual entries of the dataset is lower bounded by a decreasing function of the privacy budget. In this case, we also show that the maximal information leakage is bounded by the privacy budget. In addition to privacy guarantees, we prove that noiselessly-private mechanisms admit composition theorem and post-processing does not weaken their privacy guarantees. We prove that quantization operators can ensure noiseless privacy if the number of quantization levels is appropriately selected based on the sensitivity of the query and the privacy budget. Finally, we illustrate the privacy merits of noiseless privacy using multiple datasets in energy and transport.