Privacy‐preserving data search and sharing protocol for social networks through wireless applications

Wang, Xiaofen; Mu, Yi; Chen, Rongmao

doi:10.1002/cpe.3870

Cited by 6 publications

(3 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, many efforts have been made to study privacy-preserving data publishing. [15][16][17][18] Partition-based anonymization techniques, such as k-anonymity, l-diversity, t-closeness, 10 and m-invariance, 19 are dominantly discussed in previous years. Now, differential privacy 5,20 has regarded as the de facto standard for private data publishing.…”

Section: Privacy-preserving Data Publishingmentioning

confidence: 99%

See 1 more Smart Citation

A multi‐dimensional index for privacy‐preserving queries in cloud computing

Ding

Jin

2019

Concurrency and Computation

View full text Add to dashboard Cite

Although cloud computing saves the cost of enterprises and individuals to manage data in various applications on public cloud servers, it also causes the problem of the leakage of critical personal information and sensitive data. Therefore, the issues about data privacy of the published personal information have become a challenging problem. Most of the existing methods focus on cryptography-based techniques by encrypting the sensitive data before publishing. However, these techniques are either too computation expensive or only some authorized users can get access to the encrypted cloud data. The consequence is that the search response time is not satisfactory and the sharing extent of the cloud data is becoming limited. Motivated by this, we propose a perturbation-based method to preserve the privacy of data in cloud. To accelerate the query processing without privacy breaches, a high-efficiency multi-dimensional index is built for answering range counting queries under differential privacy, which embeds privacy-preserving R-tree index in Content Addressable Network. Experimental results demonstrate that our methods not only protect data privacy in different degree, but also accelerate the query speed efficiently. Compared with the existing method Quad-opt, our method provides about 20% better data utility under the same differential privacy principles.

show abstract

Section: Privacy-preserving Data Publishingmentioning

confidence: 99%

“…Recently, many efforts have been made to study privacy‐preserving data publishing 15–18 . Partition‐based anonymization techniques, such as k ‐anonymity, l ‐diversity, t ‐closeness, 10 and m ‐invariance, 19 are dominantly discussed in previous years.…”

Section: Related Workmentioning

confidence: 99%

A multi‐dimensional index for privacy‐preserving queries in cloud computing

Ding

Jin

2019

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…Xiaofen Wang et al . propose a new privacy‐preserving data search and sharing protocol for social networks. The protocol leverages an ID‐based multi‐user searchable encryption scheme to achieve data search pattern privacy‐preserving, anonymity, and request unlinkability.…”

mentioning

confidence: 99%

Security and privacy in social networks

Xiang

Bertino

Kutyłowski

2017

Concurrency and Computation

View full text Add to dashboard Cite

This special issue collates a selection of representative research articles that were primarily presented at the 9th International Conference on Network and System Security. This annual conference brings together researchers and practitioners from both academia and industry who are working on security and privacy in computer systems and social networks, in order to promote an exchange of ideas, discuss future collaborations, and develop new research directions.Online social networks have pervaded all aspects of our daily lives. With their unparalleled popularity, online social networks have evolved from platforms for social communication and news dissemination, to indispensable tools for professional networking, social recommendations, marketing, and online content distribution. Because of their scale, complexity, and heterogeneity, many technical and social challenges in online social networks must be addressed. It has been widely recognized that security and privacy are the critical issues in online social networks. This special issue presents many examples of how researchers, scholars, vendors, and practitioners are collaborating to address security and privacy research challenges.The scope of this special issue is broad and is representative of the multi-disciplinary nature of privacy and security. In addition to submissions that deal with malicious attacks, information control and detection, privacy protection, network data analytics for security and privacy, trust and reputation in social networks, this issue also includes articles that address practical challenges with privacy-preserving data publishing and efficient data encryption schemes.Protecting the security and privacy of user data in the context of social networks is a central topic of this issue. Xiaofen Wang et al.[3] propose a new privacy-preserving data search and sharing protocol for social networks. The protocol leverages an ID-based multi-user searchable encryption scheme to achieve data search pattern privacy-preserving, anonymity, and request unlinkability. Majed Alrubian et al.[4] describe a novel approach for finding credible sources among Twitter social network users to detect and prevent various malicious activities. They combine analysis of the user's reputation on a given topic, as well as a measure of the user's sentiment to identify topically relevant and credible sources of information. Shuhong Chen et al.[9] propose a new multi-dimensional fuzzy trust evaluation method for mobile social networks. They construct implicit social behavioral graphs based on dynamic complex community structures to infer trust relations between users. Zechao Liu et al.[6] propose a new offline and online attribute-based encryption scheme with verifiable outsourced decryption. Using the proposed scheme, the majority of the computational workload in decryption can be outsourced to third parties. Chunyong Yin et al. [8] propose an improved anonymity model for big data security based on clustering algorithm. The model integrates K-anonymity with L-divers...

show abstract