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Chunming Tang

Researcher at Guangzhou University

Publications -  16
Citations -  175

Chunming Tang is an academic researcher from Guangzhou University. The author has contributed to research in topics: Cloud computing & Encryption. The author has an hindex of 4, co-authored 16 publications receiving 144 citations.

Papers
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Book ChapterDOI

A CCA-Secure identity-based conditional proxy re-encryption without random oracles

TL;DR: A new unidirectional single-hop Identity-Based Conditional Proxy Re-Encryption (IBCPRE) scheme that not only captures the property of IBPRE, but also supports conditional re-encryption and can be proved secure against adaptive condition and adaptive identity chosen-ciphertext attacks in the standard model.
Journal ArticleDOI

Chosen-ciphertext secure multi-hop identity-based conditional proxy re-encryption with constant-size ciphertexts

TL;DR: An MH-IBPRE is proposed that maintains the (constant) ciphertext size and computational complexity regardless of the number of re-encryption hops and is proven secure against selective identity and chosen-ciphertext attacks and collusion resistant in the standard model.
Book ChapterDOI

A Conditional Proxy Broadcast Re-Encryption Scheme Supporting Timed-Release

TL;DR: This paper introduces a new notion called Timed-Release Conditional Proxy Broadcast Re-Encryption (TRCPBRE), and proposes a concrete construction for TR-CPBRE which can be proven selective identity adaptive CCA secure under the (P,Q, f)- general decisional Diffie-Hellman exponent assumption, and chosen-time period chosen-ciphertext secure under.
Journal ArticleDOI

Secure outsourced computation of the characteristic polynomial and eigenvalues of matrix

TL;DR: This paper uses data hiding technique to design a secure and verifiable outsourcing protocol for computing the characteristic polynomial and eigenvalues of a matrix and achieves several desired features, such as data privacy, verifiability and efficiency.
Posted Content

Privacy-Preserving Face Recognition with Outsourced Computation.

TL;DR: A privacy-preserving face recognition protocol with outsourced computation for the first time, which efficiently protects individuals’ privacy and substantially improves the online computation cost by outsourcing large computation task to a cloud server who has large computing power.