An Efficient Ranked Multi-Keyword Search for Multiple Data Owners Over Encrypted Cloud Data

Peng, Tianyue; Lin, Yaping; Yao, Xin; Zhang, Wei

doi:10.1109/access.2018.2828404

Cited by 42 publications

(30 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Encryption is a classic and effective way to secure sensitive user data, which has been investigated for a long time. In [5], Peng T. et al brought forth a multi-keywords sortingbased secure search method, which adopt the symmetric public key search encryption way to permit a user to make secure information retrieval in an encrypted dataset based on multiple keywords. The advantage is that it realizes secure service protection for cloud computing with limited resources.…”

Section: A Encryptionmentioning

confidence: 99%

Multi-Source Medical Data Integration and Mining for Healthcare Services

et al. 2020

View full text Add to dashboard Cite

With the advent of Internet of Health (IoH) age, traditional medical or healthy services are gradually migrating to the Web or Internet and have been producing a considerable amount of medical data associated with patients, doctors, medicine, medical infrastructure and so on. Effective fusion and analyses of these IoH data are of positive significances for the scientific disaster diagnosis and medical care services. However, IoH data are often distributed across different departments and contain partial user privacy. Therefore, it is often a challenging task to effectively integrate or mine the sensitive IoH data, during which user privacy is not disclosed. To overcome the above difficulty, we put forward a novel multi-source medical data integration and mining solution for better healthcare services, named PDFM (Privacy-free Data Fusion and Mining). Through PDFM, we can search for similar medical records in a time-efficient and privacy-preserving manner, so as to offer patients with better medical and health services. A group of experiments are enacted and implemented to demonstrate the feasibility of the proposal in this work.

show abstract

Section: A Encryptionmentioning

confidence: 99%

Multi-Source Medical Data Integration and Mining for Healthcare Services

et al. 2020

View full text Add to dashboard Cite

show abstract

“…To address the three challenges, we propose a Secure and Efficient Spatial Task Matching scheme (SESTM). We firstly adopt multi-user searchable encryption [17], [22] to preserve the location privacy, whereby the differences from previous works are that we improve query efficiency and achieve user revocation. Then, we design a tree-based index based on segment tree [13], [19], [23] to realize high matching efficiency, and the differences from previous schemes are that we reduce the query time complexity from O(n) to O(logn).…”

Section: ) Multi-user Searchable Encryption Technique Is a Generalmentioning

confidence: 99%

“…Proof: Since our encryption scheme is based on PRMSM [17] and TBMSM [22], thus the security analysis of SESTM is similar as PRMSM and TBMSM, which ensures that no additional information except the ciphertexts will be leaked to adversaries. This proof process can be referred to the Theorem 1 and Theorem 2 in TBMSM.…”

Section: G Security Analysismentioning

confidence: 99%

A Secure and Efficient Task Matching Scheme for Spatial Crowdsourcing

Zhou

Lin

et al. 2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

The sharing economy has greatly promoted the rapid development and application of spatial crowdsourcing. Although privacy-preserving spatial task matching as an indispensable part has been extensively explored, existing schemes cannot be deployed into the practical environment due to drawbacks in the one-side location protection, the matching efficiency, and the dynamic updates. In this study, we propose a novel Secure and Efficient Spatial Task Matching framework (SESTM) with utilizing multiuser searchable encryption and secure index technique, which enables to preserve the location privacy of requesters and workers while achieving efficient task allocation and good user scalability. Specifically, requesters firstly transform and encrypt their task locations before being outsourced, and we secondly design a secure and dynamic tree-based index SD-Tree for SC-server to merge these uploaded encrypted data without knowing their underlying content. Finally, SESTM provides efficient task matching services for multiple workers based on encrypted queries. Furthermore, SD-Tree also provides fast delete and insert operations under logarithmic time to reduce the dynamic update overhead for real SC services. Extensive theoretical analysis and performance evaluation demonstrate the practicality of our method.

show abstract

“…Tianyue et al suggested a secured search protocol that permits disparate DO to encrypt the files and indexes with disparate keys. Without knowing DO, the privacy‐preserving search protocol permits the CS to execute the secured MKRS.…”

Section: Related Workmentioning

confidence: 99%

Secure and privacy‐preserving keyword search retrieval over hashed encrypted cloud data

Sathyabalaji¹,

Komarasamy

S³

2019

Int J Communication

View full text Add to dashboard Cite

Summary Cloud computing (CC) is the universal area in which the data owners will contract out their pertinent data to the untrusted public cloud that permits the data users to retrieve the data with complete integrity. To give data privacy along with integrity, majority of the research works were concentrated on single data owner for secure searching of encrypted data via the cloud. Also, searchable encryption supports data user to retrieve the particular encrypted document from encrypted cloud data via keyword search (KS). However, these researches are not efficient for keyword search retrieval. To trounce such drawbacks, this paper proposes efficient secure and privacy‐preserving keyword search retrieval (SPKSR) system, in which the user retrieves the hashed encrypted documents over hashed encrypted cloud data. The proposed system includes three entities explicitly, (a) data owner (DO), (b) cloud server (CS), and (c) data users (DU). The owner outsources hashed encrypted documents set, along with generated searchable index tree to the CS. The CS hoards the hashed encrypted document collection and index tree structure. DU performs the “search” over the hashed encrypted data. Experimental results of the proposed system are analyzed and contrasted with the other existent system to show the dominance of the proposed system.

show abstract

An Efficient Ranked Multi-Keyword Search for Multiple Data Owners Over Encrypted Cloud Data

Cited by 42 publications

References 30 publications

Multi-Source Medical Data Integration and Mining for Healthcare Services

Multi-Source Medical Data Integration and Mining for Healthcare Services

A Secure and Efficient Task Matching Scheme for Spatial Crowdsourcing

Secure and privacy‐preserving keyword search retrieval over hashed encrypted cloud data

Contact Info

Product

Resources

About