April 2023 Shield: Secure Allegation Escrow System with Stronger Guarantees Authors: Koti, N., Kukkala, V. B., Patra, A., & Gopal, B. R The rising issues of harassment, exploitation, corruption, and other forms of abuse have led victims to seek comfort by acting in unison against common perpetrators (e.g., #MeToo movement). One way to curb these […]
December 2022 Find thy neighbourhood: Privacy-preserving local clustering Authors: Koti, Nishat, Varsha Bhat Kukkala, Arpita Patra, and Bhavish Raj Gopal Identifying a cluster around a seed node in a graph, termed local clustering, finds use in several applications, including fraud detection, targeted advertising, community detection, etc. However, performing local clustering is challenging when the graph […]
Aug 2022 Pentagod: Stepping beyond traditional god with five parties Authors: Koti, N., Kukkala, V. B., Patra, A., & Raj Gopal, B. Secure multiparty computation (MPC) is increasingly being used to address privacy issues in various applications. The recent work of Alon et al. (CRYPTO’20) identified the shortcomings of traditional MPC and defined a Friends-and-Foes […]
December 2023 Performance Characterization of Containerized DNN Training and Inference on Edge Accelerators Authors: Prashanthi S.K., Vinayaka Hegde, Keerthana Patchava, Ankita Das and Yogesh Simmhan Edge devices have typically been used for DNN in-ferencing. The increase in the compute power of accelerated edges is leading to their use in DNN training also. As privacy becomes […]
Aug 2024 Optimizing Federated Learning using Remote Embeddings for Graph Neural Networks Authors: Pranjal Naman and Yogesh Simmhan, Graph Neural Networks (GNNs) have experienced rapid advancements in recent years due to their ability to learn meaningful representations from graph data structures. Federated Learning (FL) has emerged as a viable machine learning approach for training a shared model on decentralized data, […]
April 2024 Minimizing Layerwise Activation Norm Improves Generalization in Federated Learning Authors: M. Yashwanth, G. K. Nayak, H. Rangwani, A. Singh, R. V. Babu, A. Chakraborty Federated Learning (FL) is an emerging machine learning framework that enables multiple clients (coordinated by a server) to collaboratively train a global model by aggregating the locally trained models without […]
December 2022 Continual Mean Estimation Under User-Level Privacy Authors: A. J. George, L. Ramesh, A. V. Singh and H. Tyagi 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 […]
User-Level Differentially Private Mean Estimation for Real-World Datasets Authors: V. A. Rameshwar, A. Tandon, and A. Sharma 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 […]
Jan 2024 Empowering SMPC: Bridging the Gap Between Scalability, Memory Efficiency and Privacy in Neural Network Inference Authors: R. Burra, A. Tandon and S. Mittal 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 […]
April 2024 Mean Estimation with User-Level Privacy for Spatio-Temporal IoT Datasets Authors: P. Gupta, V. A. Rameshwar, A. Tandon and N. Chakraborty 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 […]