作者
Qingqing Ye, Haibo Hu, Xiaofeng Meng, Huadi Zheng
发表日期
2019/5/19
研讨会论文
2019 IEEE Symposium on Security and Privacy (SP)
页码范围
317-331
出版商
IEEE
简介
Local differential privacy (LDP), where each user perturbs her data locally before sending to an untrusted data collector, is a new and promising technique for privacy-preserving distributed data collection. The advantage of LDP is to enable the collector to obtain accurate statistical estimation on sensitive user data (e.g., location and app usage) without accessing them. However, existing work on LDP is limited to simple data types, such as categorical, numerical, and set-valued data. To the best of our knowledge, there is no existing LDP work on key-value data, which is an extremely popular NoSQL data model and the generalized form of set-valued and numerical data. In this paper, we study this problem of frequency and mean estimation on key-value data by first designing a baseline approach PrivKV within the same "perturbation-calibration" paradigm as existing LDP techniques. To address the poor estimation …
引用总数
20192020202120222023202482733435016
学术搜索中的文章
Q Ye, H Hu, X Meng, H Zheng - 2019 IEEE Symposium on Security and Privacy (SP), 2019