Continual Mean Estimation Under User-Level Privacy
Authors: A. J. George, L. Ramesh, A. V. Singh and H. Tyagi Know more We consider the problem of continually releasing an estimate of the population mean of a stream of samples that is user-level differentially private (DP). At each time instant, a user contributes a sample, and the users can arrive in arbitrary order. […]
User-Level Differentially Private Mean Estimation for Real-World Datasets
Authors: V. A. Rameshwar, A. Tandon, and A. Sharma Know more In this work, we provide rigorous theoretical justifications for the performance trends of well-known clipping-based algorithms on real-world ITMS and i.i.d. synthetic datasets. An important contribution of this work is the formalization and explicit computation of the “worst-case estimation error” incurred by a canonical […]
Empowering SMPC: Bridging the Gap Between Scalability, Memory Efficiency and Privacy in Neural Network Inference
Authors: R. Burra, A. Tandon and S. Mittal Know more This paper aims to develop an efficient open-source Secure Multi-Party Computation (SMPC) repository, that addresses the issue of practical and scalable implementation of SMPC protocol on machines with moderate computational resources while aiming to reduce the execution time. We implement the ABY2.0 protocol for SMPC, […]
Mean Estimation with User-Level Privacy for Spatio-Temporal IoT Datasets
Authors: P. Gupta, V. A. Rameshwar, A. Tandon and N. Chakraborty Know more This paper considers the problem of the private release of sample means of speed values from traffic datasets. Our key contribution is the development of user-level differentially private algorithms that incorporate carefully chosen parameter values to ensure low estimation errors on real-world […]