基于频率的挖掘任务的差分私有字符串清理

Huiping Chen, Changyu Dong, Liyue Fan, G. Loukides, S. Pissis, L. Stougie
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引用次数: 1

摘要

字符串用于模拟基因组、自然语言和网络活动数据,因此经常被广泛共享。然而,字符串数据共享引起了隐私问题,因为字符串的长度为k的子字符串及其频率(多重度)的知识可能足以唯一地重建字符串;由此推断出的这些子字符串可能会泄露机密信息。因此,我们引入了通过应用差分隐私(DP)来保护单个字符串S的长度为k的子字符串的问题,同时最大化基于频率的挖掘任务的数据效用。我们的理论和经验证据表明,经典的DP机制不适合解决这个问题。作为回应,我们采用了S的k阶德布鲁因图G,并提出了一种基于采样的机制来在G上执行DP。我们考虑了使用我们的机制在G上执行DP的任务,同时保持G的归一化边多重性。我们定义了一个整数边权重的优化问题,这是该任务的核心,并开发了一种基于动态规划的算法来精确解决它。我们还考虑了这个问题的两个具有实际边权的变体。通过放宽整数边权的约束,我们能够为这些变量开发线性时间精确的算法,我们将其用作有效启发式的垫脚石。使用真实世界的大规模字符串(按数十亿个字母的顺序)进行的广泛实验评估表明,我们的启发式方法是有效的,并产生了近乎最优的解决方案,为基于频率的挖掘任务保留了数据效用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Differentially Private String Sanitization for Frequency-Based Mining Tasks
Strings are used to model genomic, natural language, and web activity data, and are thus often shared broadly. However, string data sharing has raised privacy concerns stemming from the fact that knowledge of length-k substrings of a string and their frequencies (multiplicities) may be sufficient to uniquely reconstruct the string; and from that the inference of such substrings may leak confidential information. We thus introduce the problem of protecting length-k substrings of a single string S by applying Differential Privacy (DP) while maximizing data utility for frequency-based mining tasks. Our theoretical and empirical evidence suggests that classic DP mechanisms are not suitable to address the problem. In response, we employ the order-k de Bruijn graph G of S and propose a sampling-based mechanism for enforcing DP on G. We consider the task of enforcing DP on G using our mechanism while preserving the normalized edge multiplicities in G. We define an optimization problem on integer edge weights that is central to this task and develop an algorithm based on dynamic programming to solve it exactly. We also consider two variants of this problem with real edge weights. By relaxing the constraint of integer edge weights, we are able to develop linear-time exact algorithms for these variants, which we use as stepping stones towards effective heuristics. An extensive experimental evaluation using real-world large-scale strings (in the order of billions of letters) shows that our heuristics are efficient and produce near-optimal solutions which preserve data utility for frequency-based mining tasks.
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