Privacy-Aware Randomized Quantization via Linear Programming
This work addresses the need for discrete-valued differential privacy mechanisms in data analytics, offering an incremental improvement over prior methods.
The paper tackles the problem of generating unbiased discrete outputs under differential privacy, which existing quantization mechanisms fail to achieve due to bias or poor accuracy-privacy trade-offs. It proposes a family of unbiased quantization mechanisms optimized via linear programming, resulting in a better privacy-accuracy trade-off compared to baselines.
Differential privacy mechanisms such as the Gaussian or Laplace mechanism have been widely used in data analytics for preserving individual privacy. However, they are mostly designed for continuous outputs and are unsuitable for scenarios where discrete values are necessary. Although various quantization mechanisms were proposed recently to generate discrete outputs under differential privacy, the outcomes are either biased or have an inferior accuracy-privacy trade-off. In this paper, we propose a family of quantization mechanisms that is unbiased and differentially private. It has a high degree of freedom and we show that some existing mechanisms can be considered as special cases of ours. To find the optimal mechanism, we formulate a linear optimization that can be solved efficiently using linear programming tools. Experiments show that our proposed mechanism can attain a better privacy-accuracy trade-off compared to baselines.