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Electronic Colloquium on Computational Complexity

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REPORTS > AUTHORS > DIVESH AGGARWAL:
All reports by Author Divesh Aggarwal:

TR26-095 | 4th June 2026
Divesh Aggarwal, Rishav Gupta, Hai Hoang Nguyen, Kel Zin Tan, Prashant Nalini Vasudevan

Towards Worst-case Hardness for Low-Noise LPN

The hardness of the Learning Parity with Noise (LPN) problem is a foundational assumption in cryptography, forming the basis of constructions ranging from symmetric-key primitives to public-key encryption and beyond. A central open question is whether the average-case hardness of LPN can be based on worst-case complexity assumptions, as has ... more >>>


TR26-011 | 15th January 2026
Divesh Aggarwal, Zihan Li, Saswata Mukherjee, Maciej Obremski, João Ribeiro

Complete Characterization of Randomness Extraction from DAG-Correlated Sources

Revisions: 1

We introduce the SHEDAG (Somewhere Honest Entropic sources over Directed Acyclic Graphs) source model, a general model for multi-block randomness sources with causal correlations.
A SHEDAG source is defined over a directed acyclic graph (DAG) $G$ whose nodes output $n$-bit blocks. Blocks output by honest nodes are independent (by ... more >>>


TR24-077 | 19th April 2024
Divesh Aggarwal, JinMing Leong, Alexandra Veliche

Worst-Case to Average-Case Hardness of LWE: A Simple and Practical Perspective

Revisions: 5

In this work, we study the worst-case to average-case hardness of the Learning with Errors problem (LWE) under an alternative measure of hardness - the maximum success probability achievable by a probabilistic polynomial-time (PPT) algorithm. Previous works by Regev (STOC 2005), Peikert (STOC 2009), and Brakerski, Peikert, Langlois, Regev, Stehle ... more >>>




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