
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…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Wireless Communication Security Techniques · Cryptography and Data Security
