Hui Zhu

TL;DR: The general architecture of big data analytics is formalized, the corresponding privacy requirements are identified, and an efficient and privacy-preserving cosine similarity computing protocol is introduced as an example in response to data mining's efficiency and privacy requirements in the big data era.

TL;DR: It is shown that eDiag can ensure that users’ health information and healthcare provider's prediction model are kept confidential, and has significantly less computation and communication overhead than existing schemes.

TL;DR: This paper proposes a new verifiable fuzzy keyword search scheme based on the symbol-tree which not only supports the fuzzy keywords search, but also enjoys the verifiability of the searching result.

TL;DR: A new efficient and privacy-preserving LBS query scheme in outsourced cloud, i.e., EPQ, for pervasive smartphones, based on an improved homomorphic encryption technique over a composite order group, a special spatial range query algorithm SRQC over ciphertext is proposed, with which EPQ achieves privacy preservation of user's query and confidentiality of LBS data in the outsourcing cloud server.

TL;DR: Within CINEMA framework, users can access online medical primary diagnosing service accurately without divulging their medical data and the diagnosis result can only be decrypted by the user, meanwhile, the diagnosis model in SP can also be protected.