Interests and expertise

Dr. Sarkar’s primary focus is on creating privacy-preserving solutions with high levels of assurance, which can be proven through formal security models and various cryptographic techniques. These techniques may include Secure Computation and Zero-Knowledge, among others.

At Supra, he is working on improving the state-of-the-art primitives used in the blockchain space, mainly Verifiable Randomness Services, Threshold Cryptosystems and Zero-Knowledge.

Recently, he has been also been intrigued by the potential of Applied Cryptography to utilize statistical analysis on client data without compromising their privacy.

Commentary

Secure Computation is a powerful tool that enables mutually distrusting parties to compute a joint function on their private inputs, without compromising the secrecy of their inputs. This enables different applications like privacy-preserving machine learning, private-data sharing among hospitals, private measurement of advertisement effectiveness, etc.

However, general secure computation is expensive in terms of communication and computation. His research focuses on building customized secure computation protocols for different real-world use-cases that are faster in practice.

Selected research publications