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Enhancing Privacy and Efficiency of Distributed Encryption

Series: Ph.D. Colloquium

Speaker: Anirban Chakrabarti Ph.D. (Engg) student Department of Computer Science and Engineering

Date/Time: Apr 28 10:00:00

Location: CSA Auditorium, (Room No. 104, Ground Floor)

Faculty Advisor: Prof. Bhavana Kanukurthi

Abstract:
This work enhances privacy and efficiency in distributed encryption systems, which aim to eliminate dependence on central trusted au thorities. In such systems, users generate their own key materials and communicate via broadcast channels or untrusted servers. The first contribution introduces a deduplication scheme with formal definitions and practical constructions. It enables users to share ciphertext only for redundant file portions, preserving the privacy of unique content. This is achieved through data-adaptive clustering, where similar files are dynamically grouped during encryption based on incoming data patterns. The schemes efficiency is evaluated using real-world datasets, including images and genomic sequences, across various similarity metrics such as Hamming, set difference, and edit distance. The second contribution focuses on threshold decryption in a distributed t-out-of-n setting, where any t parties can decrypt ciphertexts. Traditional systems are vulnerable if t colluding parties create a decoder to decrypt any ciphertext. To counter this, the proposed system integrates a tracing mechanism that identifies participants who misuse their decryption keys. This discourages unauthorized decryption services and introduces accountability. Such traceable threshold decryption is particularly useful in applications requiring high integrity and confidentiality, including encrypted mempools, private voting, and sealed-bid auctions.

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