论文标题
负责任的私人设定基数用于分布式测量
Accountable Private Set Cardinality for Distributed Measurement
论文作者
论文摘要
我们引入了密码协议,以牢固有效地计算设置联合和设置相交的基数。我们的私人设定核心方案协议(PSC)是为分布式系统中一大批政党进行观察的设置而设计的,并且一小部分具有更多资源和更高可靠性汇总的政党。 PSC允许在隐私保护分布式系统中收集安全且有用的统计信息。例如,它允许TOR等匿名网络的运营商安全回答以下问题:“有多少唯一用户正在使用该网络?”和“正在访问多少个隐藏服务?”。 我们证明了普遍合成性框架中PSC的正确性和安全性,以损害除汇总当事方以外的所有方面的主动对手。尽管在这种情况下无法保证成功的产出,但PSC成功或终止了中止,我们进一步使对手负责造成至少一个恶意政党来造成中止。我们还表明,PSC阻止了数据各方的自适应腐败揭示过去的观察结果,这阻止了他们成为有针对性妥协的受害者,并且我们通过使产出差异私密来确保安全测量。 我们提出了PSC的概念验证实现,并使用它来证明PSC的计算开销低和合理的带宽。它可以计算成千上万的独特观察结果,从数十到数百个数据收集各方,而在几个小时内完成。因此,PSC适用于分布式系统中的每日测量。
We introduce cryptographic protocols for securely and efficiently computing the cardinality of set union and set intersection. Our private set-cardinality protocols (PSC) are designed for the setting in which a large set of parties in a distributed system makes observations, and a small set of parties with more resources and higher reliability aggregates the observations. PSC allows for secure and useful statistics gathering in privacy-preserving distributed systems. For example, it allows operators of anonymity networks such as Tor to securely answer the questions: "How many unique users are using the network?" and "How many hidden services are being accessed?". We prove the correctness and security of PSC in the Universal Composability framework against an active adversary that compromises all but one of the aggregating parties. Although successful output cannot be guaranteed in this setting, PSC either succeeds or terminates with an abort, and we furthermore make the adversary accountable for causing an abort by blaming at least one malicious party. We also show that PSC prevents adaptive corruption of the data parties from revealing past observations, which prevents them from being victims of targeted compromise, and we ensure safe measurements by making outputs differentially private. We present a proof-of-concept implementation of PSC and use it to demonstrate that PSC operates with low computational overhead and reasonable bandwidth. It can count tens of thousands of unique observations from tens to hundreds of data-collecting parties while completing within hours. PSC is thus suitable for daily measurements in a distributed system.