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Mahak Pancholi

dblp:265/5970 · DBLP profile ↗
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9ranked-venue papers
0as first author
9since 2021 · last 2026
0009-0006-5317-5535ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Security and privacy · 9 · 9 since 2021Theory of computation · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Network and information security
6 papers
Cryptographic protocols and secure computation · 83% Cryptographic primitives and cryptanalysis · 12% Privacy and data protection · 4%
Artificial intelligence
1 paper
Trustworthy machine learning · 100%

Topics — the 14 heaviest of 15, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Cryptographic protocols and secure computation › proof systems
zero-knowledge proofs
2.642025
Fiat-Shamir Bulletproofs are Non-malleable (in the Random Oracle Model) · J. Cryptol. 2025
Witness-Succinct Universally-Composable SNARKs · EUROCRYPT (2) 2023
Fiat-Shamir Bulletproofs are Non-Malleable (in the Algebraic Group Model) · EUROCRYPT (2) 2022
Cryptographic protocols and secure computation › proof systems › zero-knowledge proofs
bulletproofs
1.422025
Fiat-Shamir Bulletproofs are Non-malleable (in the Random Oracle Model) · J. Cryptol. 2025
Fiat-Shamir Bulletproofs are Non-Malleable (in the Algebraic Group Model) · EUROCRYPT (2) 2022
Cryptographic primitives and cryptanalysis › provable security › security notions
non-malleability
1.422025
Fiat-Shamir Bulletproofs are Non-malleable (in the Random Oracle Model) · J. Cryptol. 2025
Fiat-Shamir Bulletproofs are Non-Malleable (in the Algebraic Group Model) · EUROCRYPT (2) 2022
Cryptographic protocols and secure computation
secure multiparty computation
1.222023
Ramen: Souper Fast Three-Party Computation for RAM Programs · CCS 2023
Reverse Firewalls for Adaptively Secure MPC Without Setup · ASIACRYPT (2) 2021
Cryptographic protocols and secure computation › secure computation protocols
secure computation with RAM programs
0.712023
Ramen: Souper Fast Three-Party Computation for RAM Programs · CCS 2023
Cryptographic protocols and secure computation › proof systems
succinct non-interactive arguments of knowledge
0.712023
Witness-Succinct Universally-Composable SNARKs · EUROCRYPT (2) 2023
Cryptographic protocols and secure computation › secure multiparty computation
three-party computation
0.712023
Ramen: Souper Fast Three-Party Computation for RAM Programs · CCS 2023
Cryptographic protocols and secure computation › coin flipping
adaptively secure coin flipping
0.512021
Reverse Firewalls for Adaptively Secure MPC Without Setup · ASIACRYPT (2) 2021
Cryptographic protocols and secure computation › secure multiparty computation
adaptively secure MPC
0.512021
Reverse Firewalls for Adaptively Secure MPC Without Setup · ASIACRYPT (2) 2021
Cryptographic protocols and secure computation
coin flipping
0.512021
Reverse Firewalls for Adaptively Secure MPC Without Setup · ASIACRYPT (2) 2021
Privacy and data protection
privacy-preserving machine learning
0.512021
SWIFT: Super-fast and Robust Privacy-Preserving Machine Learning · USENIX Security Symposium 2021
Cryptographic protocols and secure computation › subversion resilience
reverse firewalls
0.512021
Reverse Firewalls for Adaptively Secure MPC Without Setup · ASIACRYPT (2) 2021
Cryptographic protocols and secure computation › composable security
universally composable security
0.212023
Witness-Succinct Universally-Composable SNARKs · EUROCRYPT (2) 2023
Machine learning › Trustworthy machine learning › privacy › privacy-preserving machine learning
privacy-preserving distributed learning
0.112021
SWIFT: Super-fast and Robust Privacy-Preserving Machine Learning · USENIX Security Symposium 2021

Methods — techniques the papers use, named apart from their topics

fiat-shamir transform · 1.4random oracle model · 0.9RAM program evaluation · 0.7algebraic group model · 0.6secure multiparty computation · 0.5secure multi-party computation · 0.5reverse firewalls · 0.5
YearPublicationVenuePosition
2026 On Composing AGM-Secure Functionalities with Cryptographic Proofs - Applications to Unbounded-Depth IVC and More
Matteo Campanelli, Dario Fiore 0001, Mahak Pancholi
PKC (3)3
2025 Securely Computing One-Sided Matching Markets
James Hsin-yu Chiang, Ivan Damgård, Claudio Orlandi, Mahak Pancholi, Mark Simkin 0001
FC4
2025 Fiat-Shamir Bulletproofs are Non-malleable (in the Random Oracle Model)
Chaya Ganesh, Claudio Orlandi, Mahak Pancholi, Akira Takahashi 0002, Daniel Tschudi
J. Cryptol.3
2023 Ramen: Souper Fast Three-Party Computation for RAM Programs
abstract
Secure RAM computation allows a number of parties to evaluate a function represented as a random-access machine (RAM) program in a way that reveals nothing about the private inputs of the parties except from what is already revealed by the function output itself. In this work we present Ramen, which is a new protocol for computing RAM programs securely among three parties, tolerating up to one passive corruption. Ramen provides reasonable asymptotic guarantees and is concretely efficient at the same time. We have implemented our protocol and provide extensive benchmarks for various settings.
Lennart Braun, Mahak Pancholi, Rahul Rachuri, Mark Simkin 0001
CCS2
2023 Witness-Succinct Universally-Composable SNARKs
abstract
Zero-knowledge Succinct Non-interactive ARguments of Knowledge (zkSNARKs) are becoming an increasingly fundamental tool in many real-world applications where the proof compactness is of the utmost importance, including blockchains. A proof of security for SNARKs in the Universal Composability (UC) framework (Canetti, FOCS’01) would rule out devastating malleability attacks. To retain security of SNARKs in the UC model, one must show their simulation-extractability such that the knowledge extractor is both black-box and straight-line , which would imply that proofs generated by honest provers are non-malleable . However, existing simulation-extractability results on SNARKs either lack some of these properties, or alternatively have to sacrifice witness succinctness to prove UC security. In this paper, we provide a compiler lifting any simulation-extractable NIZKAoK into a UC-secure one in the global random oracle model, importantly, while preserving the same level of witness succinctness. Combining this with existing zkSNARKs, we achieve, to the best of our knowledge, the first zkSNARKs simultaneously achieving UC-security and constant sized proofs.
Chaya Ganesh, Yashvanth Kondi, Claudio Orlandi, Mahak Pancholi, Akira Takahashi 0002, Daniel Tschudi
EUROCRYPT (2)4
2023 How to Compile Polynomial IOP into Simulation-Extractable SNARKs: A Modular Approach
Markulf Kohlweiss, Mahak Pancholi, Akira Takahashi 0002
TCC (3)2
2022 Fiat-Shamir Bulletproofs are Non-Malleable (in the Algebraic Group Model)
abstract
Bulletproofs (Bünz et al. IEEE S&P 2018) are a celebrated ZK proof system that allows for short and efficient proofs, and have been implemented and deployed in several real-world systems. In practice, they are most often implemented in their non-interactive version obtained using the Fiat-Shamir transform, despite the lack of a formal proof of security for this setting. Prior to this work, there was no evidence that malleability attacks were not possible against Fiat-Shamir Bulletproofs. Malleability attacks can lead to very severe vulnerabilities, as they allow an adversary to forge proofs re-using or modifying parts of the proofs provided by the honest parties. In this paper, we show for the first time that Bulletproofs (or any other similar multi-round proof system satisfying some form of weak unique response property) achieve simulation-extractability in the algebraic group model . This implies that Fiat-Shamir Bulletproofs are non-malleable .
Chaya Ganesh, Claudio Orlandi, Mahak Pancholi, Akira Takahashi 0002, Daniel Tschudi
EUROCRYPT (2)3
2021 Reverse Firewalls for Adaptively Secure MPC Without Setup
abstract
We study Multi-party computation (MPC) in the setting of subversion, where the adversary tampers with the machines of honest parties. Our goal is to construct actively secure MPC protocols where parties are corrupted adaptively by an adversary (as in the standard adaptive security setting), and in addition, honest parties’ machines are compromised.The idea of reverse firewalls (RF) was introduced at EUROCRYPT’15 by Mironov and Stephens-Davidowitz as an approach to protecting protocols against corruption of honest parties’ devices. Intuitively, an RF for a party \(\mathcal {P}\) is an external entity that sits between \(\mathcal {P}\) and the outside world and whose scope is to sanitize \(\mathcal {P}\)’s incoming and outgoing messages in the face of subversion of their computer. Mironov and Stephens-Davidowitz constructed a protocol for passively-secure two-party computation. At CRYPTO’20, Chakraborty, Dziembowski and Nielsen constructed a protocol for secure computation with firewalls that improved on this result, both by extending it to multi-party computation protocol, and considering active security in the presence of static corruptions.In this paper, we initiate the study of RF for MPC in the adaptive setting. We put forward a definition for adaptively secure MPC in the reverse firewall setting, explore relationships among the security notions, and then construct reverse firewalls for MPC in this stronger setting of adaptive security. We also resolve the open question of Chakraborty, Dziembowski and Nielsen by removing the need for a trusted setup in constructing RF for MPC.Towards this end, we construct reverse firewalls for adaptively secure augmented coin tossing and adaptively secure zero-knowledge protocols and obtain a constant round adaptively secure MPC protocol in the reverse firewall setting without setup. Along the way, we propose a new multi-party adaptively secure coin tossing protocol in the plain model, that is of independent interest.
Suvradip Chakraborty, Chaya Ganesh, Mahak Pancholi, Pratik Sarkar
ASIACRYPT (2)3
2021 SWIFT: Super-fast and Robust Privacy-Preserving Machine Learning
Nishat Koti, Mahak Pancholi, Arpita Patra, Ajith Suresh
USENIX Security Symposium2