Shuffle模型的紧差分隐私毯
Tight Differential Privacy Blanket for Shuffle Model
摘要：
随着最近对数字经济的关注，个人数据的重要性最近大幅增加。随着这一趋势的发展，数据市场的模式开始成为一个以获得高质量个人信息为交换动机的过程。为了正式保证保护数字经济中涉及的敏感数据的隐私，\emph{differential privacy (DP)}是首选技术，最近引起了社区的广泛关注。然而，通过确保最高级别的隐私保护，同时保留数据的实用性，优化隐私-实用性权衡至关重要。在本文中，我们从理论上推导出充分和必要条件，有紧$(ε,\,δ)$ -DP毯的洗牌模型，据我们所知，还没有被证明之前，并因此，表征最好的可能的DP保护洗牌模型，可以在数据市场中实施，以确保隐私保护的数字经济交易。
英文摘要：
With the recent bloom of focus on digital economy, the importance of personal data has seen a massive surge of late. Keeping pace with this trend, the model of data market is starting to emerge as a process to obtain high-quality personal information in exchange of incentives. To have a formal guarantee to protect the privacy of the sensitive data involved in digital economy, \emph{differential privacy (DP)} is the go-to technique, which has gained a lot of attention by the community recently. However, it is essential to optimize the privacy-utility trade-off by ensuring the highest level of privacy protection is ensured while preserving the utility of the data. In this paper, we theoretically derive sufficient and necessary conditions to have tight $(ε,\,δ)$-DP blankets for the shuffle model, which, to the best of our knowledge, have not been proven before, and, thus, characterize the best possible DP protection for shuffle models which can be implemented in data markets to ensure privacy-preserving trading of digital economy.
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。
