论文标题
马尔可夫连锁店的最佳当地贝叶斯差异隐私
Optimal Local Bayesian Differential Privacy over Markov Chains
论文作者
论文摘要
在数据隐私的文献中，差异隐私是最受欢迎的模型。如果算法有或没有任何个人数据的输出是无法区分的，则算法是私有的。在本文中，我们专注于马尔可夫链生成的数据，并认为在这种情况下，贝叶斯差异隐私（BDP）提供了更有意义的保证。我们的主要理论贡献是在从二进制马尔可夫链中获取数据时提供了实现BDP的机制。我们改进了最先进的BDP机制，并表明我们的机制为任何局部机制提供了最佳的噪声私人关系，直到可忽略不计。我们还简要讨论了一种非本地机制，该机制增加了相关的噪声。最后，我们对合成数据进行实验，以详细说明DP不足，并对真实数据进行实验，以表明我们的隐私保证对不是简单的马尔可夫链的基础分布非常有力。
In the literature of data privacy, differential privacy is the most popular model. An algorithm is differentially private if its outputs with and without any individual's data are indistinguishable. In this paper, we focus on data generated from a Markov chain and argue that Bayesian differential privacy (BDP) offers more meaningful guarantees in this context. Our main theoretical contribution is providing a mechanism for achieving BDP when data is drawn from a binary Markov chain. We improve on the state-of-the-art BDP mechanism and show that our mechanism provides the optimal noise-privacy tradeoffs for any local mechanism up to negligible factors. We also briefly discuss a non-local mechanism which adds correlated noise. Lastly, we perform experiments on synthetic data that detail when DP is insufficient, and experiments on real data to show that our privacy guarantees are robust to underlying distributions that are not simple Markov chains.
