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Zhilin Zhang
Simon Fraser University
Distributed computingData miningEncryptionComputer scienceCloud computing
15Publications
2H-index
16Citations
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Publications 14
Newest
#1Xiaofeng Ding (HUST: Huazhong University of Science and Technology)H-Index: 9
#2Hongbiao Fang (HUST: Huazhong University of Science and Technology)
Last. Hai Jin (HUST: Huazhong University of Science and Technology)H-Index: 52
view all 5 authors...
Deep learning is increasingly popular, partly due to its widespread application potential, such as in civilian, government and military domains. Given the exacting computational requirements, cloud computing has been utilized to host user data and model. However, such an approach has potential privacy implications. Therefore, in this paper, we propose a method to protect user's privacyin the inference phase of deep learning workflow. Specifically, we use an intermediate layer to separate the ent...
Source
#1Weipeng LinH-Index: 2
#1Weipeng Lin (SZPT: Shenzhen Polytechnic)
Last. Chunyan Miao (NTU: Nanyang Technological University)
view all 7 authors...
Searchable symmetric encryption enables a cloud server to answer queries directly over encrypted data. Two key requirements are a strong security guarantee and a sub-linear search performance. The bucketization approach in the literature addresses these requirements at the expense of downloading false positives and requiring the local search at the client side. In this work, we propose a novel approach to meet these requirements while minimizing the client's work and communication cost. First, a...
Source
Nov 3, 2019 in CIKM (Conference on Information and Knowledge Management)
#1Zhilin Zhang (SFU: Simon Fraser University)H-Index: 2
#2Ke Wang (SFU: Simon Fraser University)H-Index: 52
Last. Raymond Chi-Wing Wong (HKUST: Hong Kong University of Science and Technology)H-Index: 27
view all 5 authors...
We consider the following secure data retrieval problem: a client outsources encrypted data blocks to a semi-trusted cloud server and later retrieves blocks without disclosing access patterns. Existing PIR and ORAM solutions suffer from serious performance bottlenecks in terms of communication or computation costs. To help eliminate this void, we introduce "access pattern unlinkability'' that separates access pattern privacy into short-term privacy at individual query level and long-term privacy...
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#1Zhilin ZhangH-Index: 2
#1Yuncheng Wu (RUC: Renmin University of China)H-Index: 2
#2Ke Wang (SFU: Simon Fraser University)H-Index: 52
Last. Cuiping Li (RUC: Renmin University of China)H-Index: 6
view all 7 authors...
Group k-nearest neighbor ( kGNN) search allows a group of nmobile users to jointly retrieve kpoints from a location-based service provider (LSP) that minimizes the aggregate distance to them. We identify four protection objectives in the privacy preserving kGNN search: (i) every user's location should be protected from LSP; (ii) the group's query and the query answer should be protected from LSP; (iii) LSP's private database information should be protected from users; (iv) every u...
Source
#1Weipeng Lin (SFU: Simon Fraser University)H-Index: 2
#2Ke Wang (SFU: Simon Fraser University)H-Index: 52
Last. Cheng Long (NTU: Nanyang Technological University)H-Index: 10
view all 6 authors...
Searchable symmetric encryption (SSE) enables a remote cloud server to answer queries directly over encrypted data on a client’s behalf, therefore, relieves the resource limited client from complicated data management tasks. Two key requirements are a strong security guarantee and a sub-linear search performance. The bucketization approach in the literature addresses these requirements at the expense of downloading many false positives or requiring the client to search relevant bucket ids locall...
Source
Jan 30, 2019 in WSDM (Web Search and Data Mining)
#1Dugang Liu (Ha Tai: Xiamen University)H-Index: 1
#2Chen Lin (Ha Tai: Xiamen University)H-Index: 7
Last. Hanghang Tong (ASU: Arizona State University)H-Index: 40
view all 5 authors...
It has been established that, ratings are missing not at random in recommender systems. However, little research has been done to reveal how the ratings are missing. In this paper we present one possible explanation of the missing not at random phenomenon. We verify that, using a variety of different real-life datasets, there is a spiral process for a silent minority in recommender systems where (1) people whose opinions fall into the minority are less likely to give ratings than majority opinio...
2 CitationsSource
#1Zhilin ZhangH-Index: 2
#2Ke WangH-Index: 52
Last. Raymond Chi-Wing WongH-Index: 27
view all 5 authors...
1 Citations
Oct 17, 2018 in CIKM (Conference on Information and Knowledge Management)
#1Zhilin Zhang (SFU: Simon Fraser University)H-Index: 2
#2Ke Wang (SFU: Simon Fraser University)H-Index: 52
Last. Weipeng Lin (SFU: Simon Fraser University)H-Index: 2
view all 4 authors...
Secure top-k inner product retrieval allows the users to outsource encrypted data vectors to a cloud server and at some later time find the k vectors producing largest inner products giving an encrypted query vector. Existing solutions suffer poor performance raised by the client's filtering out top-k results. To enable the server-side filtering, we introduce an asymmetric inner product encryption AIPE that allows the server to compute inner products from encrypted data and query vectors. To sol...
1 CitationsSource
Multi-valued data are commonly found in many real applications. During the process of clustering multi-valued data, most existing methods use sampling or aggregation mechanisms that cannot reflect the real distribution of objects and their instances and thus fail to obtain high-quality clusters. In this paper, a concept of \alphaapproximation distance is introduced to measure the connectivity between multi-valued objects by taking account of the distribution of the instances. An \alphaappr...
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