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Dongxiao Liu
Researcher at University of Waterloo
Publications - 33
Citations - 1035
Dongxiao Liu is an academic researcher from University of Waterloo. The author has contributed to research in topics: Encryption & Cloud computing. The author has an hindex of 11, co-authored 33 publications receiving 705 citations. Previous affiliations of Dongxiao Liu include University of Electronic Science and Technology of China & Chinese Academy of Sciences.
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Enabling Efficient Multi-Keyword Ranked Search Over Encrypted Mobile Cloud Data Through Blind Storage
TL;DR: This paper develops the searchable encryption for multi-keyword ranked search over the storage data by considering the large number of outsourced documents ( data) in the cloud and utilizing the relevance score and k-nearest neighbor techniques to develop an efficient multi- keyword search scheme.
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Anonymous Reputation System for IIoT-Enabled Retail Marketing Atop PoS Blockchain
TL;DR: This paper proposes an anonymous reputation system that preserves consumer identities and individual review confidentialities in the consumer–retailer channel and is more efficient to offer high levels of privacy guarantees compared with existing ones.
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Engineering searchable encryption of mobile cloud networks: when QoE meets QoP
TL;DR: A fine-grained data search scheme is developed and its implementation on encrypted mobile cloud data, which is an effective balance between QoE and QoP in mobile cloudData outsourcing, is discussed.
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Personalized Search Over Encrypted Data With Efficient and Secure Updates in Mobile Clouds
TL;DR: This paper improves the existing works by developing a more practical searchable encryption technique, which can support dynamic updating operations in the mobile cloud applications and proposes PSU, a Personalized Search scheme over encrypted data with efficient and secure Updates in mobile cloud.
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Achieving efficient and privacy-preserving truth discovery in crowd sensing systems
TL;DR: This paper utilizes the additive homomorphic privacy-preserving data aggregation and super-increasing sequence techniques to achieve both high performance and strong privacy protection and indicates that the EPTD can achieve confidentiality of observed values and privacy protection of users' weights.