A Hybrid Approach to Privacy-Preserving Federated Learning

Federated learning facilitates the collaborative training of models without the sharing of raw data. However, recent attacks demonstrate that simply maintaining data locality during training processes does not provide sufficient privacy guarantees. Rather, we need a federated learning system capable of preventing inference over both the messages exchanged during training and the final trained model while ensuring the resulting model also has acceptable predictive accuracy. Existing federated learning approaches either use secure multiparty computation (SMC) which is vulnerable to inference or differential privacy which can lead to low accuracy given a large number of parties with relatively small amounts of data each. In this paper, we present an alternative approach that utilizes both differential privacy and SMC to balance these trade-offs. Combining differential privacy with secure multiparty computation enables us to reduce the growth of noise injection as the number of parties increases without sacrificing privacy while maintaining a pre-defined rate of trust. Our system is therefore a scalable approach that protects against inference threats and produces models with high accuracy. Additionally, our system can be used to train a variety of machine learning models, which we validate with experimental results on 3 different machine learning algorithms. Our experiments demonstrate that our approach out-performs state of the art solutions

Differentially-private Multiparty Clustering

In an era marked by the widespread application of Machine Learning (ML) across diverse domains, the necessity of privacy-preserving techniques has become paramount. The Euclidean k-Means problem, a fundamental component of unsupervised learning, brings to light this privacy challenge, especially in federated contexts. Existing Federated approaches utilizing Secure Multiparty Computation (SMPC) or Homomorphic Encryption (HE) techniques, although promising, suffer from substantial overheads and do not offer output privacy. At the same time, differentially private k-Means algorithms fall short in federated settings. Recognizing the critical need for innovative solutions safeguarding privacy, this work pioneers integrating Differential Privacy (DP) into federated k-Means. The key contributions of this dissertation include the novel integration of DP in horizontally-federated k-Means, a lightweight aggregation protocol offering three orders of magnitude speedup over other multiparty approaches, the application of cluster-size constraints in DP k-Means to enhance state-of-the-art utility, and a meticulous examination of various aggregation methods in the protocol. Unlike traditional privacy-preserving approaches, our innovative design results in a faster, more private, and more accurate solution, significantly advancing the state-of-the-art in privacy-preserving machine learning