Proceedings ArticleDOI
Privacy-Preserving Top-k Nearest Keyword Search on Outsourced Graphs
Yiping Teng,Xiang Cheng,Sen Su,Rong Bi +3 more
- pp 815-822
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TLDR
A new privacy-preserving scheme for top-k nearest keyword search on graphs is proposed, in which a two-level secure index is devised to facilitate privacy- Preserving top-K nearest keywordSearch on graphs.Abstract:
With massive networks emerging with labels or textual contents on the nodes, keyword search on graphs has been used in a wide range of real-life applications in recent years. For achieving great cost savings, data owners are motivated to outsource their services on graphs to the cloud. However, directly outsourcing them may arise serious privacy concerns. In this paper, we study the problem of privacy-preserving top-k nearest keyword search on outsourced graphs. Only a few existing studies primarily focus on the privacy-preserving graph operations under encryption settings, which cannot be directly applied to solve the problem of privacy-preserving keyword search on graphs. To address this problem, we propose a new privacy-preserving scheme for top-k nearest keyword search on graphs, in which a two-level secure index is devised to facilitate privacy-preserving top-k nearest keyword search. To handle the keyword filtering in search processing, we also propose a trapdoor generation method based on privacy-preserving set operations. Leveraging the two-level secure index and trapdoors, we further present the privacy-preserving top-k nearest keyword search algorithm. Thorough analysis shows the validity and security of our scheme. Extensive experimental results on real datasets further demonstrate our scheme can achieve high efficiency.read more
Citations
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A Blockchain-based Approach to the Secure Sharing of Healthcare Data
Huihui Yang,Bian Yang +1 more
TL;DR: In this paper, the authors proposed a blockchain and MedRec-based approach by implementing signcryption and attribute-based authentication to enable the secure sharing of healthcare data, where all patient's fragmented EHR pieces can be viewed as a whole record and stored secure against tampering.
Journal ArticleDOI
Privacy-preserving image search (PPIS): Secure classification and searching using convolutional neural network over large-scale encrypted medical images
TL;DR: A privacy-preserving Convolutional Neural Network framework is constructed that allows the classification and searching of secure, content-based, large-scale encrypted images (including large-size medical images) with homomorphic encryption.
Journal ArticleDOI
Keyword Search on Large Graphs: A Survey
Jianye Yang,Wu Yao,Wenjie Zhang +2 more
TL;DR: This survey aims to provide the researchers a comprehensive understanding of existing graph keyword search solutions by classifying the existing works into different categories according to the specific problem definition.
Proceedings ArticleDOI
Privacy-Preserving Approximate Top-k Nearest Keyword Queries over Encrypted Graphs
TL;DR: Wang et al. as discussed by the authors proposed a new graph encryption scheme Aton, which enables efficient and privacy-preserving top-k nearest keyword (k-NK) querying, based on the symmetric-key encryption and particular pseudo-random functions.
Proceedings ArticleDOI
Secure Spatial Network Queries on Cloud Platform
TL;DR: This work defines and study Secure Spatial Network kNN Query (SSNQ) problem on cloud platform, and presents Basic Secure Sp spatial network query method, in which secure kNN is computed for the query node to construct candidate sequences using secure subprotocols.
References
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Book ChapterDOI
Public-key cryptosystems based on composite degree residuosity classes
TL;DR: A new trapdoor mechanism is proposed and three encryption schemes are derived : a trapdoor permutation and two homomorphic probabilistic encryption schemes computationally comparable to RSA, which are provably secure under appropriate assumptions in the standard model.
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Jonathan Katz,Yehuda Lindell +1 more
TL;DR: This book discusses Private-Key (Symmetric) Cryptography, Number Theory and Cryptographic Hardness Assumptions, and the Random-Oracle Model in Detail.
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Efficient Private Matching and Set Intersection
TL;DR: In this paper, the problem of computing the intersection of private datasets of two parties, where the datasets contain lists of elements taken from a large domain, was considered and protocols based on the use of homomorphic encryption and balanced hashing were proposed.
Efficient private matching and set intersection
TL;DR: This work considers the problem of computing the intersection of private datasets of two parties, where the datasets contain lists of elements taken from a large domain, and presents protocols, based on the use of homomorphic encryption and balanced hashing, for both semi-honest and malicious environments.
Journal ArticleDOI
Privacy-Preserving Multi-Keyword Ranked Search over Encrypted Cloud Data
TL;DR: This paper proposes a basic idea for the MRSE based on secure inner product computation, and gives two significantly improved MRSE schemes to achieve various stringent privacy requirements in two different threat models and further extends these two schemes to support more search semantics.