论文标题
混洗模型的紧密差别隐私毯
Tight Differential Privacy Blanket for Shuffle Model
论文作者
论文摘要
随着最近关注数字经济的关注，个人数据的重要性最近发生了巨大的激增。与这一趋势保持同步，数据市场的模型开始出现，作为获取高质量个人信息以交换激励措施的过程。为了保护数字经济中涉及的敏感数据的隐私的正式保证，\ emph {dixial隐私（dp）}是首选技术，最近社区引起了很多关注。但是，必须通过确保在保留数据效用的同时确保确保最高级别的隐私保护来优化隐私 - 实用性权衡。 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.
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.
