Continuous Release of Data Streams under both Centralized and Local Differential Privacy

ACM CCS 2021

Tianhao Wang1 Joann Qiongna Chen2 Zhikun Zhang3 Dong Su4 Yueqiang Cheng5 Zhou Li2 Ninghui Li6 Somesh Jha7

1. Carnegie Mellon University 2. University of California, Irvine 3. CISPA Helmholtz Center for Information Security 4. Alibaba Inc. 5. NIO Security Research 6. Purdue University 7. University of Wisconsin, Madison


In this paper, we study the problem of publishing a stream of real-valued data satisfying differential privacy (DP). One major challenge is that the maximal possible value can be quite large; thus it is necessary to estimate a threshold so that numbers above it are truncated to reduce the amount of noise that is required to all the data. The estimation must be done based on the data in a private fashion. We develop such a method that uses the Exponential Mechanism with a quality function that approximates well the utility goal while maintaining a low sensitivity. Given the threshold, we then propose a novel online hierarchical method and several post-processing techniques. Building on these ideas, we formalize the steps into a framework for private publishing of stream data. Our framework consists of three components: a threshold optimizer that privately estimates the threshold, a perturber that adds calibrated noises to the stream, and a smoother that improves the result using post-processing. Within our framework, we design an algorithm satisfying the more stringent setting of DP called local DP (LDP). To our knowledge, this is the first LDP algorithm for publishing streaming data. Using four real-world datasets, we demonstrate that our mechanism outperforms the state-of-the-art by a factor of 6-10 orders of magnitude in terms of utility (measured by the mean squared error of answering a random range query).



    author = {Tianhao Wang and Joann Qiongna Chen and Zhikun Zhang and Dong Su and Yueqiang Cheng and Zhou Li and Ninghui Li and Somesh Jha},
    title = {{Continuous Release of Data Streams under both Centralized and Local Differential Privacy}},
    booktitle = {{ACM CCS}},
    publisher = {},
    year = {2021},