CP-ABE Based Privacy-Preserving User Profile Matching in Mobile Social Networks

Weirong, Cui; Du, Chenglie; Chen, Jinchao

doi:10.1371/journal.pone.0157933

Cited by 5 publications

(3 citation statements)

References 22 publications

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“…The literature [19] proposed a hierarchical management scheme that uses attributes to match friends, which aims to promote social network users to find friends safely and efficiently. In order to quickly match friends, the literature [20] designed a new CP-ABE-scheme-based privacy protection attribute matching scheme for mobile social networks, in which users could efficiently match friends almost without interaction.…”

Section: Related Workmentioning

confidence: 99%

A Privacy-Preserving Friend Matching Scheme Based on Attribute Encryption in Mobile Social Networks

Yu,

Nan,

Niu

2024

Electronics

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In mobile social networks, users can easily communicate with others through smart devices. Therefore, the protection of user privacy in social networks is becoming a significant subject. To solve this problem, this paper proposes a fine-grained data access control scheme that uses attributes to match friends. In our scheme, the friend-making parties generate friend preference and self-description lists, respectively, realizing attribute hiding by converting friendship preference into ciphertext access control policies and self-description into attribute keys. The social platform matches user profiles to quickly eliminate unmatched users and avoids invalid decryption. In order to reduce the computational burden and communication cost of mobile devices, we adopt an algorithm mechanism for outsourcing decryption. When the user meets the matching conditions, the algorithm outsources the bilinear pair operation with large computation to the friend server. After that, the user finally decrypts the ciphertext. Security analysis shows that our scheme is safe and effective. In addition, performance evaluation shows that the proposed scheme is efficient and practical.

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Section: Related Workmentioning

confidence: 99%

A Privacy-Preserving Friend Matching Scheme Based on Attribute Encryption in Mobile Social Networks

Yu,

Nan,

Niu

2024

Electronics

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“…Inadequate privacy policies pose a significant challenge in mobile social networks (MSNs), as users often accept privacy policies without fully understanding their implications. Privacy policies outline how user data is collected, stored, used, and shared by the MSN platform and its affiliates [49]. When privacy policies are unclear, overly complex, or lack transparency, users may unknowingly agree to terms that compromise their privacy and expose their personal information to risks.…”

Section: Inadequate Privacy Policiesmentioning

confidence: 99%

An Overview of using of Artificial Intelligence in Enhancing Security and Privacy in Mobile Social Networks

Fakhouri,

Alawadi,

Awaysheh

et al. 2023

2023 Eighth International Conference on Fog and Mobile Edge Computing (FMEC)

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Mobile Social Networks (MSNs) have emerged as pivotal platforms for communication, information dissemination, and social connection in contemporary society. As their prevalence escalates, so too do concerns regarding security and privacy. This paper presents a furnishes a detailed analysis of these pressing issues and elucidates how Artificial Intelligence (AI) can be instrumental in addressing them. The study thoroughly explores a spectrum of security and privacy challenges endemic to MSNs, such as data leakage, unauthorized access, cyberstalking, location privacy, and more. Additionally, the investigation expands to encompass problems like impersonation, phishing attacks, malware threats, information overload, user profiling, inadequate privacy policies, third-party application vulnerabilities, and privacy issues related to photos, videos, end-to-end encryption, Wi-Fi connections, and data retention. Each of these issues is dissected in depth, highlighting the potential risks and implications for users. Furthermore, the paper underlines how AI can provide in mitigating these problems, establishing its fundamental role in fortifying the security and privacy of MSNs. This thorough analysis offers valuable insights and feasible solutions using AI to bolster security and privacy in the ever-evolving landscape of Mobile Social Networks.

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“…Li et al [32] proposed a point-to-point pre-matching scheme, using Bloom filter to reduce the computational load of users performing decryption of CP-ABE, and they elaborated how to establish a verifiable secure communication channel between matched users. Based on this, Cui et al [16] designed a receiver anonymous attribute matching scheme using CP-ABE and bloom filter. Qi et al [15] combined searchable encryption with CP-ABE and proposed a friend discovery protocol with hidden attributes and fine-grained access control.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Blockchain-Based Bidirectional Friends Matching Scheme in Social Networks

Yang

Wang

et al. 2020

IEEE Access

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In social networks, the personal attributes or hobbies of the users are exposed to the server to establish the relationships. Service providers may store these information for commercial purpose or statistical analysis. Furthermore, the server may expose to external attacks, which may disclose users' privacy information. In this paper, we present a hierarchical blockchain-based attribute matching scheme, which realizes privacy-preserving attribute matching under multiple semi-trusted servers. The scheme employs CP-ABE and bloom filter to satisfy the requirements of the users to make friend discovery, and reduces the computation cost of users by outsourcing decryption of CP-ABE. Besides, the hierarchical blockchain only implements the consensus and storage of matching results on the blockchain, while the complex calculations and a large amount of data storage are off-chain, which reduces the consumption of the blockchain and improves the operation efficiency. Finally, we prove the scheme can resist single point failure, collusion attack, internal attack and external attack, the experimental results demonstrate the proposed scheme is feasibility and efficiency.

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CP-ABE Based Privacy-Preserving User Profile Matching in Mobile Social Networks

Cited by 5 publications

References 22 publications

A Privacy-Preserving Friend Matching Scheme Based on Attribute Encryption in Mobile Social Networks

A Privacy-Preserving Friend Matching Scheme Based on Attribute Encryption in Mobile Social Networks

An Overview of using of Artificial Intelligence in Enhancing Security and Privacy in Mobile Social Networks

An Efficient Blockchain-Based Bidirectional Friends Matching Scheme in Social Networks

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