ABSTRACT

Online social networks (OSNs) are nowadays an important field of applications thanks to the recent surge in online interaction. However, the illegal disclosure of user's private data can cause damaging consequences and even threaten the safety of users' life. The privacy issues of OSNs have become a matter of great concern for many people. In recent years, there are some research works to address this privacy issue, yet they do not always focus on providing the normal social network services for users, such as data sharing, data retrieval and data access services. Therefore, it is a challenge to ensure the security of sensitive data while providing efficient and privacy-preserving social network services for users. In this paper, we propose a novel blockchain-based privacy-preserving framework for online social networks, called BPP. Combined blockchain and public-key cryptography technique, the BPP framework can achieve secure data sharing, data retrieving, and data accessing with fairness and without worrying about potential damage to users' interest. Specifically, based on blockchain and public key encryption with keyword search technique, a secure, fair and efficient keyword search algorithm is proposed, with which the BBP framework realises privacy preservation of user's query and then obtain accurate query results with assurance and without needing for any further verification operation in online social network. Finally, we implement a prototype of our framework and deploy it to a locally simulated network. The extensive experiments and security analysis demonstrate the security, efficacy and efficiency of our proposed framework.

摘要

由于最近在线交互的激增，在线社交网络（OSN）如今已成为一个重要的应用领域。但是，非法泄露用户隐私数据会造成破坏性后果，甚至威胁到用户的生命安全。OSN的隐私问题已经成为很多人非常关心的问题。近年来，有一些研究工作解决了这个隐私问题，但它们并不总是专注于为用户提供正常的社交网络服务，例如数据共享、数据检索和数据访问服务。因此，在为用户提供高效且隐私保护的社交网络服务的同时，确保敏感数据的安全是一个挑战。在本文中，我们为在线社交网络提出了一种新的基于区块链的隐私保护框架，称为 BPP。BPP框架结合区块链和公钥密码技术，可以实现安全的数据共享、数据检索和数据访问，公平公正，无需担心对用户利益的潜在损害。具体而言，基于区块链和关键字搜索技术的公钥加密，提出了一种安全、公平、高效的关键字搜索算法，通过该算法，BBP框架实现了用户查询的隐私保护，从而有把握地获得准确的查询结果，无需任何在线社交网络中的进一步验证操作。最后，我们实现了我们框架的原型并将其部署到本地模拟网络。广泛的实验和安全分析证明了我们提出的框架的安全性、有效性和效率。