Differential Privacy in Distributed Settings

Data is considered the “new oil” in the information society and digital economy. While many commercial activities and government decisions are based on data, the public raises more concerns about privacy leakage when their private data are collected and used. In this dissertation, we investigate the privacy risks in settings where the data are distributed across multiple data holders, and there is only an untrusted central server. We provide solutions for several problems under this setting with a security notion called differential privacy (DP). Our solutions can guarantee that there is only limited and controllable privacy leakage from the data holder, while the utility of the final results, such as model prediction accuracy, can be still comparable to the ones of the non-private algorithms. First, we investigate the problem of estimating the distribution over a numerical domain while satisfying local differential privacy (LDP). Our protocol prevents privacy leakage in the data collection phase, in which an untrusted data aggregator (or a server) wants to learn the distribution of private numerical data among all users. The protocol consists of 1) a new reporting mechanism called the square wave (SW) mechanism, which randomizes the user inputs before sharing them with the aggregator; 2) an Expectation Maximization with Smoothing (EMS) algorithm, which is applied to aggregated histograms from the SW mechanism to estimate the original distributions. First, we investigate the problem of estimating the distribution over a numerical domain while satisfying local differential privacy (LDP). Our protocol prevents privacy leakage in the data collection phase, in which an untrusted data aggregator (or a server) wants to learn the distribution of private numerical data among all users. The protocol consists of 1) a new reporting mechanism called the square wave (SW) mechanism, which randomizes the user inputs before sharing them with the aggregator; 2) an Expectation Maximization with Smoothing (EMS) algorithm, which is applied to aggregated histograms from the SW mechanism to estimate the original distributions. Second, we study the matrix factorization problem in three federated learning settings with an untrusted server, i.e., vertical, horizontal, and local federated learning settings. We propose a generic algorithmic framework for solving the problem in all three settings. We introduce how to adapt the algorithm into differentially private versions to prevent privacy leakage in the training and publishing stages. Finally, we propose an algorithm for solving the k-means clustering problem in vertical federated learning (VFL). A big challenge in VFL is the lack of a global view of each data point. To overcome this challenge, we propose a lightweight and differentially private set intersection cardinality estimation algorithm based on the Flajolet-Martin (FM) sketch to convey the weight information of the synopsis points. We provide theoretical utility analysis for the cardinality estimation algorithm and further refine it for better empirical performance