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A Framework for Protecting Privacy on Mobile Social Networks
Mobile Networks and Applications ( IF 3.8 ) Pub Date : 2021-05-29 , DOI: 10.1007/s11036-021-01761-1
Seyyed Mohammad Safi , Ali Movaghar , Komeil Safikhani Mahmoodzadeh

In recent years, mobile social networks have largely been developed and gained considerable popularity. An approach to protecting privacy on mobile social networks is the use of encryption and access control. Good alternatives for use on mobile social networks are the Public Broadcast Encryption approach for appropriate concordance and consistency with the structure of social networks as well as the Attribute-Based Encryption owing to its capability and proper implementation of the access control policy. Accordingly, in this paper, a framework was presented based on the Public Broadcast Encryption and Attribute-Based Encryption. Using proxies, we outsourced some of these operations in the proposed framework to reduce the computational load of the end device, accelerate the encryption and decryption operations, and decrease the amount of storage memory for keeping the encryption and decryption parameters required by the users in the end-device. Cloud was also employed to store the shared data and user preferences in the social network. The results of investigating the privacy parameters reveal that our framework is superior to the four compared methods. Additionally, the results indicate that in terms of three important parameters in mobile social networks, namely communication, computation, and storage complexity, our method has less complexity and overhead than they have.



中文翻译:

移动社交网络隐私保护框架

近年来,移动社交网络得到了很大的发展并获得了相当大的普及。在移动社交网络上保护隐私的一种方法是使用加密和访问控制。在移动社交网络上使用的良好替代方案是公共广播加密方法,用于与社交网络结构适当的一致性和一致性,以及基于属性的加密,因为它具有访问控制策略的能力和正确实施。因此,在本文中,提出了一个基于公共广播加密和基于属性的加密的框架。使用代理,我们在提议的框架中外包了其中一些操作,以减少终端设备的计算负载,加速加密和解密操作,并减少用于保存终端设备中用户所需的加密和解密参数的存储内存量。云还被用来在社交网络中存储共享数据和用户偏好。调查隐私参数的结果表明,我们的框架优于四种比较方法。此外,结果表明,就移动社交网络中的三个重要参数,即通信、计算和存储复杂度而言,我们的方法具有比它们更低的复杂度和开销。调查隐私参数的结果表明,我们的框架优于四种比较方法。此外,结果表明,就移动社交网络中的三个重要参数,即通信、计算和存储复杂度而言,我们的方法具有比它们更低的复杂度和开销。调查隐私参数的结果表明,我们的框架优于四种比较方法。此外,结果表明,就移动社交网络中的三个重要参数,即通信、计算和存储复杂度而言,我们的方法具有比它们更低的复杂度和开销。

更新日期:2021-05-30
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