R$^2$DP: A Universal and Automated Approach to Optimizing the Randomization Mechanisms of Differential Privacy for Utility Metrics with No Known Optimal Distributions
Differential privacy (DP) has emerged as a de facto standard privacy notion for a wide range of applications. Since the meaning of data utility in different applications may vastly differ, a key challenge is to find the optimal randomization mechanism, i.e., the distribution and its parameters, for a given utility metric. Existing works have identified the optimal distributions in some special cases, while leaving all other utility metrics (e.g., usefulness and graph distance) as open problems. Since existing works mostly rely on manual analysis to examine the search space of all distributions, it would be an expensive process to repeat such efforts for each utility metric. To address such deficiency, we propose a novel approach that can automatically optimize different utility metrics found in diverse applications under a common framework. Our key idea that, by regarding the variance of the injected noise itself as a random variable, a two-fold distribution may approximately cover the search space of all distributions. Therefore, we can automatically find distributions in this search space to optimize different utility metrics in a similar manner, simply by optimizing the parameters of the two-fold distribution. Specifically, we define a universal framework, namely, randomizing the randomization mechanism of differential privacy (R$^2$DP), and we formally analyze its privacy and utility. Our experiments show that R$^2$DP can provide better results than the baseline distribution (Laplace) for several utility metrics with no known optimal distributions, whereas our results asymptotically approach to the optimality for utility metrics having known optimal distributions. As a side benefit, the added degree of freedom introduced by the two-fold distribution allows R$^2$DP to accommodate the preferences of both data owners and recipients.
LMO-DP: Optimizing the Randomization Mechanism for Differentially Private Fine-Tuning (Large) Language Models
University of Connecticut（康涅狄格大学）；Iowa State University（爱荷华州立大学）；University of Kansas（堪萨斯大学）；Cisco Research（思科研究院）；Illinois Institute of Technology（伊利诺伊理工学院）
Frequency Estimation of Evolving Data Under Local Differential Privacy
收集和分析不断变化的纵向数据已成为一种普遍做法。在这种情况下，保护用户隐私的一种可能方法是使用本地差分隐私（LDP）协议，该协议确保即使在违规或数据滥用的情况下也能保护所有用户的隐私保护。现有的LDP数据汇集协议，例如Google的RAPPOR和Microsoft的dBitFlipPM，可以具有与域大小k成线性的纵向隐私，这对于大域，例如互联网域来说是过大的。为了解决这个问题，在本文中，我们介绍了一种新的LDP数据汇集协议的纵向频率监测命名为LOLOHA LOCAL HASING（LOLOHA）正式的隐私保证。此外，我们的协议的隐私效用权衡是线性的，相对于减少域大小$2\leq g \ll k$。LOLOHA结合了通过本地哈希的域缩减方法和双重随机化，以最大限度地减少数据更新引起的隐私泄漏。正如我们的理论分析以及我们的实验评估所证明的那样，LOLOHA实现了与当前最先进的协议相竞争的效用，同时大大减少了纵向隐私预算消耗高达k/g数量级。
