11 papers
Optimal Domain-Aware Privacy Mechanisms for Synthetic Data Generation
Sajani Vithana, Sangwon Jung, Haoyang Hu +3
Differential privacy (DP) imposes fundamental trade-offs between privacy and statistical fidelity in synthetic data generation. While access to public data has been shown to improv…
Local Differential Privacy with Correlated Noise Achieves Central-DP Optimal Cost
Madhura Pathegama, Srikanth Avasarala, Viveck R. Cadambe +1
We study privately estimating the sum of user-held values in the presence of an honest-but-curious server. This motivates requiring privacy not only at data release but also th…
FLARE: One-Shot PE-Level Fault Localization in Systolic Arrays via Algebraic Test Vectors
Logashree Venkatasubramanian, Zishen Wan, Viveck Cadambe
Systolic arrays are the dominant compute fabric for neural network inference. Prior work has addressed column-level fault detection efficiently with uniform test patterns, but row-…
Differentially Private Secure Multiplication: Beyond Two Multiplicands
Haoyang Hu, Viveck R. Cadambe
We study the problem of differentially private (DP) secure multiplication in distributed computing systems, focusing on regimes where perfect privacy and perfect accuracy cannot be…
Game of Coding: Sybil Resistant Decentralized Machine Learning with Minimal Trust Assumption
Hanzaleh Akbari Nodehi, Viveck R. Cadambe, Mohammad Ali Maddah-Ali
Coding theory plays a crucial role in ensuring data integrity and reliability across various domains, from communication to computation and storage systems. However, its reliance o…
Game of Coding: Coding Theory in the Presence of Rational Adversaries, Motivated by Decentralized Machine Learning
Hanzaleh Akbari Nodehi, Viveck R. Cadambe, Mohammad Ali Maddah-Ali
Coding theory plays a crucial role in enabling reliable communication, storage, and computation. Classical approaches assume a worst-case adversarial model and ensure error correct…