字符串和向量数据库的非自适应Mastermind算法及实例研究
Nonadaptive Mastermind Algorithms for String and Vector Databases, with Case Studies
摘要：
在本文中，我们研究了稀疏利用Mastermind算法攻击隐私的整个数据库的字符串或向量，如DNA字符串，电影评级，或社交网络的友谊数据。基于对非适应性群体测试的简化，我们的方法能够利用最小量的隐私泄露，例如包含在单个比特中，指示医学数据库中的两个人是否有任何共同的基因突变，或者两个人是否在在线社交网络中有任何共同的朋友。我们分析我们的主谋攻击算法使用理论表征，提供克隆数据库所需的查询数量的次线性界限，以及基因组信息，协同过滤数据和在线社交网络的实验测试。通过利用这些现实世界的数据库和调制的参数，控制查询稀疏性的一般稀疏的性质，我们证明了相对较少的非自适应查询需要恢复每个数据库的大部分。
英文摘要：
In this paper, we study sparsity-exploiting Mastermind algorithms for attacking the privacy of an entire database of character strings or vectors, such as DNA strings, movie ratings, or social network friendship data. Based on reductions to nonadaptive group testing, our methods are able to take advantage of minimal amounts of privacy leakage, such as contained in a single bit that indicates if two people in a medical database have any common genetic mutations, or if two people have any common friends in an online social network. We analyze our Mastermind attack algorithms using theoretical characterizations that provide sublinear bounds on the number of queries needed to clone the database, as well as experimental tests on genomic information, collaborative filtering data, and online social networks. By taking advantage of the generally sparse nature of these real-world databases and modulating a parameter that controls query sparsity, we demonstrate that relatively few nonadaptive queries are needed to recover a large majority of each database.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
