270 research outputs found
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
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