More Like this

1. A Practical Oblivious Cloud Storage System based on TEE and Client Gateway

   https://doi.org/10.1109/PST52912.2021.9647827  

   Zhang, Wensheng (December 2021, International Conference on Privacy, Security and Trust (PST))
2. Packet Scheduling with Optional Client Privacy

   https://doi.org/10.1145/3460120.3485371  

   Beams, Andrew; Kannan, Sampath; Angel, Sebastian (November 2021, ACM SIGSAC Conference on Computer and Communications Security (CCS ’21))

   xisting network switches implement scheduling disciplines such as FIFO or deficit round robin that provide good utilization or fairness across flows, but do so at the expense of leaking a variety of information via timing side channels. To address this privacy breach, we propose a new scheduling mechanism for switches called indifferent-first scheduling (IFS). A salient aspect of IFS is that it provides privacy (a notion of strong isolation) to clients that opt-in, while preserving the (good) performance and utilization of FIFO or round robin for clients that are satisfied with the status quo. Such a hybrid scheduling mechanism addresses the main drawback of prior proposals such as time-division multiple access (TDMA) that provide strong isolation at the cost of low utilization and increased packet latency for all clients. We identify limitations of modern programmable switches which inhibit an implementation of IFS without compromising its privacy guarantees, and show that a version of IFS with full security can be implemented at line rate in the recently proposed push-in-first-out (PIFO) queuing architecture. 

   more » « less
3. Federated Minimax Optimization with Client Heterogeneity

   Sharma, Pranay; Panda, Rohan; Joshi, Gauri (December 2023, Transactions on machine learning research)

   Minimax optimization has seen a surge in interest with the advent of modern applications such as GANs, and it is inherently more challenging than simple minimization. The difficulty is exacerbated by the training data residing at multiple edge devices or clients, especially when these clients can have heterogeneous datasets and heterogeneous local computation capabilities. We propose a general federated minimax optimization framework that subsumes such settings and several existing methods like Local SGDA. We show that naive aggregation of model updates made by clients running unequal number of local steps can result in optimizing a mismatched objective function – a phenomenon previously observed in standard federated minimization. To fix this problem, we propose normalizing the client updates by the number of local steps. We analyze the convergence of the proposed algorithm for classes of nonconvex-concave and nonconvex-nonconcave functions and characterize the impact of heterogeneous client data, partial client participation, and heterogeneous local computations. For all the function classes considered, we significantly improve the existing computation and communication complexity results. Experimental results support our theoretical claims. 

   more » « less
4. Client-Aided Privacy-Preserving Machine Learning

   https://doi.org/10.1007/978-3-031-71070-4_10  

   Miao, Peihan; Shi, Xinyi; Wu, Chao; Xu, Ruofan (September 2024, Springer Cham)

   Privacy-preserving machine learning (PPML) enables multiple distrusting parties to jointly train ML models on their private data without revealing any information beyond the final trained models. In this work, we study the client-aided two-server setting where two non-colluding servers jointly train an ML model on the data held by a large number of clients. By involving the clients in the training process, we develop efficient protocols for training algorithms including linear regression, logistic regression, and neural networks. In particular, we introduce novel approaches to securely computing inner product, sign check, activation functions (e.g., ReLU, logistic function), and division on secret shared values, leveraging lightweight computation on the client side. We present constructions that are secure against semi-honest clients and further enhance them to achieve security against malicious clients. We believe these new client-aided techniques may be of independent interest. We implement our protocols and compare them with the two-server PPML protocols presented in SecureML (Mohassel and Zhang, S&P’17) across various settings and ABY2.0 (Patra et al., Usenix Security’21) theoretically. We demonstrate that with the assistance of untrusted clients in the training process, we can significantly improve both the communication and computational efficiency by orders of magnitude. Our protocols compare favorably in all the training algorithms on both LAN and WAN networks. 

   more » « less
5. Federated Learning via Indirect Server-Client Communications

   https://doi.org/10.1109/CISS56502.2023.10089783  

   Bian, Jieming; Shen, Cong; Xu, Jie (March 2023, Annual Conference on Information Sciences and Systems (CISS))