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cs.IT2026

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…

cs.IT2026

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…

cs.IT2026

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…

cs.IT2026

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…

cs.IT2025

Game of Coding: Beyond Honest-Majority Assumptions

Hanzaleh Akbari Nodehi, Viveck R. Cadambe, Mohammad Ali Maddah-Ali

Coding theory revolves around the incorporation of redundancy into transmitted symbols, computation tasks, and stored data to guard against adversarial manipulation. However, error…

cs.IT2025

Differentially Private Secure Multiplication with Erasures and Adversaries

Haoyang Hu, Viveck R. Cadambe

We consider a private distributed multiplication problem involving N computation nodes and T colluding nodes. Shamir's secret sharing algorithm provides perfect information-theoret…