Deep Inder Mohan

Published in the proceedings of CRYPTO 2024; Co-author: Joseph Jaeger

The Fuchsbauer, Kiltz, and Loss (Crypto 2018) claim that (some) hardness results in the algebraic group model imply the same hardness results in the generic group model was recently called into question by Katz, Zhang, and Zhou (Asiacrypt 2022). The latter gave an interpretation of the claim under which it is incorrect. We give an alternate interpretation under which it is correct, using natural frame- works for capturing generic and algebraic models for arbitrary algebraic structures. Most algebraic analyses in the literature can be captured by our frameworks, making the claim correct for them.

Published in the Taylor & Francis Journal of Simulation. Co-authors: Arjun Verma, Shrisha Rao

Existing studies of the multi-group dynamics of prejudiced societies focus on the social-psychological knowledge behind the relevant processes. Instead, we create a multi-agent framework that simulates the propagation of prejudice and measures its tangible impact on prosperity. Our simulations show that even modeling prejudice as an exclusively out-group phenomenon nonetheless generates implicit in-group promotion, leading to higher relative prosperity of a prejudiced population. This model is a step towards a deeper understanding of the origins, propagation, and ramifications of prejudice through simulative studies grounded in apt theoretical backgrounds.

(Ongoing) In collaboration with Sasha Boldyreva & Tianxin Tang

Our objective is to create a biometrics-based key retrieval system that does not require users to remember any additional secrets beyond their biometrics, and yet achieves security against brute-force biometric attackers.

Master's thesis @ IIITB under the supervision of Prof. Srinivas Vivek

Foundational ID systems such as SSN and AADHAR can be critical to the effective governance of a nation. Since these systems deal with data that is inherently critical, securing this data becomes paramount. This poses a challenge to smaller nations, that do not have the resources to set up in-house servers to run these systems. In this project, we develop a Homomorphic Encryption based system that allows nations to levarage (potentially) untrusted third-party servers for storing and processing user data while ensuring that this data would remain secure - even if the third-party is breached. The accepted thesis can be found here. This work was also presented by Prof. Srinivas Vivek in the Turing foundation’s Trustworthy Digital Identity Conference held in London, U.K. on Sept. 16, 2022.