Picture of Sinjoni Mukhopadhyay

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Sinjoni Mukhopadhyay

Sinjoni joined the Storage Systems Research Center in Winter 2016. She is a fourth-year Computer Science Ph.D. candidate working under Professors Darrell Long and Ethan Miller. She is expected to graduate in early 2021.

Research Interests:

Generative Adversarial Networks, synthetic workload generation, archival storage, storage security, information retrieval, cloud storage architecture, data privacy in the cloud, cryptography and secure data in Internet-of-Things.

Current Project:

Collecting and disseminating real system workloads is difficult both logistically and from a security and privacy standpoint, yet obtaining synthetic workloads that are sufficiently representative for tuning tasks is difficult and ungeneralizable. She is developing a realistic synthetic workload generator called Zoroaster that uses Generative Adversarial Networks to produce synthetic traces that are not only scalable but also statistically accurate when compared to real-world enterprise traces. 

Past Projects:

To avoid the complexities of key management and constant updates due to encryption algorithm deprecation, authentication is preferred over encryption to securely store archival data. SSRC has had projects like POTSHARDS in the past that has been used to reconstruct secret-split archival datastores.  The Secret-Split Secure Hash Method (S3HA) is an extension of POTSHARDS that quickly reduces the reconstruction space of secret-split datastore required to identify sibling shares. 


 

 

Publications

Last modified 15 May 2019