论文标题
用不同的隐私学习
Learning With Differential Privacy
论文作者
论文摘要
如果数据包含敏感信息，数据的泄漏可能对个人级别产生了极大的影响。常见的预防方法，例如加密 - 终点保护，入侵检测系统易于泄漏。差异隐私进行了救援，并有适当的保护防止泄漏的承诺，因为它在收集数据时使用了随机响应技术，该技术有望通过更好的效用来强大的隐私。差异隐私使人们可以通过描述群体模式而无需披露任何单独的树木来访问数据森林。领先的科技公司和学术界目前对差异隐私的改编鼓励作者详细探讨该主题。将讨论差异隐私的不同方面，它在隐私保护和信息泄漏中的应用，对该领域当前研究方法的比较讨论，其在现实世界中以及权衡权衡的实用性。
The leakage of data might have been an extreme effect on the personal level if it contains sensitive information. Common prevention methods like encryption-decryption, endpoint protection, intrusion detection system are prone to leakage. Differential privacy comes to the rescue with a proper promise of protection against leakage, as it uses a randomized response technique at the time of collection of the data which promises strong privacy with better utility. Differential privacy allows one to access the forest of data by describing their pattern of groups without disclosing any individual trees. The current adaption of differential privacy by leading tech companies and academia encourages authors to explore the topic in detail. The different aspects of differential privacy, it's application in privacy protection and leakage of information, a comparative discussion, on the current research approaches in this field, its utility in the real world as well as the trade-offs - will be discussed.
