Privacy-preserving Profile Matching Research Articles

Blockchain-Empowered Secure and Privacy-Preserving Health Data Sharing in Edge-Based IoMT

Health data sharing, as a booming demand, enables the patients with similar symptoms to connect with each other and doctors to obtain the medical history of patients. Health data are usually collected from edge-based Internet of medical things (IoMT) with devices such as smart wearable devices, smart watches, and smartphones. Since health data are highly private and have great financial value, adversaries ceaselessly launch diverse attacks to obtain private information. All these issues pose great challenges to health data sharing in edge-based IoMT scenarios. Existing research either lacks comprehensive consideration of privacy and security protection or fails to provide a proper incentive mechanism, which expels users from sharing data. In this study, we propose a novel blockchain-assisted data sharing scheme, which allows secure and privacy-preserving profile matching. A bloom filter with hash functions is designed to verify the authenticity of keyword ciphertext. Key-policy attribute-based encryption (KP-ABE) algorithm and smart contracts are employed to achieve secure profile matching. To incentivize users actively participating in profile matching, we devise an incentive mechanism and construct a two-phase Stackelberg game to address pricing problems for data owners and accessing problems of data requesters. The optimal pricing mechanism is specially designed for encouraging more users to participate in health data sharing and maximizing users’ profit. Moreover, security analysis illustrates that the proposed protocol is capable of satisfying various security goals, while performance evaluation shows high scalability and feasibility of the proposed scheme in edge-based IoMT scenarios.

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

Privacy-preserving profile matching, a challenging task in mobile social networks, is getting more attention in recent years. In this paper, we propose a novel scheme that is based on ciphertext-policy attribute-based encryption to tackle this problem. In our scheme, a user can submit a preference-profile and search for users with matching-profile in decentralized mobile social networks. In this process, no participant’s profile and the submitted preference-profile is exposed. Meanwhile, a secure communication channel can be established between the pair of successfully matched users. In contrast to existing related schemes which are mainly based on the secure multi-party computation, our scheme can provide verifiability (both the initiator and any unmatched user cannot cheat each other to pretend to be matched), and requires few interactions among users. We provide thorough security analysis and performance evaluation on our scheme, and show its advantages in terms of security, efficiency and usability over state-of-the-art schemes.

Privacy Preserving Profile Matching System for Trust-Aware Personalized User Recommendations in Social Networks

Privacy Preserving Profile Matching System for Trust-Aware Personalized User Recommendations in Social Networks

A Review on Trust-Aware and Privacy Preserving Profile Matching System for Personalized User Recommendations in Social networks

is becoming a very important part of social network from the security point of view. In this project, we introduce a framework for handling trust in social networks, which is based on reputation mechanism. The reputation mechanism captures the implicit and explicit connections between the network members, analyses the semantics and dynamics of these connections, and provides personalized user recommendations to the network members. Based on the trust semantics, the system will provide the positive (trustworthy) recommendations and the negative (untrustworthy) recommendations. Along with this our system provides one more interesting mode i.e. public profile matching that preserves privacy on social networks. This profile matching contributes in reputation ratings required for suggestions of friend list. In order to compute the reputation of each member, we adopt several other properties of trust such as, transitivity, personalization, and context, and draw ideas from sociology axioms. Trust is not perfectly transitive in social networks, in that trust decays along the transition path, but it is generally agreed that it can be communicated between people. Along with trust generation percentile of profile matching is also considered for personal recommendation.

Privacy-Preserving Distributed Profile Matching in Proximity-Based Mobile Social Networks

Making new connections according to personal preferences is a crucial service in mobile social networking, where an initiating user can find matching users within physical proximity of him/her. In existing systems for such services, usually all the users directly publish their complete profiles for others to search. However, in many applications, the users' personal profiles may contain sensitive information that they do not want to make public. In this paper, we propose FindU, a set of privacy-preserving profile matching schemes for proximity-based mobile social networks. In FindU, an initiating user can find from a group of users the one whose profile best matches with his/her; to limit the risk of privacy exposure, only necessary and minimal information about the private attributes of the participating users is exchanged. Two increasing levels of user privacy are defined, with decreasing amounts of revealed profile information. Leveraging secure multi-party computation (SMC) techniques, we propose novel protocols that realize each of the user privacy levels, which can also be personalized by the users. We provide formal security proofs and performance evaluation on our schemes, and show their advantages in both security and efficiency over state-of-the-art schemes.