人本感知网络中用户位置信息保护的研究

摘要

人本感知网络作为一种新型的无线传感器网络系统，在多个领域中发挥着重要的作用。然而，由于语义攻击等新型攻击方式的出现，用户位置等隐私信息可能被攻击者获取。因此针对用户实际需求，设计适合人本感知网络且能抵抗语义攻击等新型攻击手段的用户位置信息保护机制是一个重要且具有挑战性的工作。文章首先通过结合地理位置语义属性和隐匿空间技术降低攻击者利用语义攻击定位用户位置的概率；然后结合BP神经网络技术提出新的隐匿空间选取算法，让用户在不传输自身实际位置信息的情况下获得合适的隐匿空间，以消除攻击者利用隐匿空间生成器和服务器等不可信第三方获取用户位置隐私信息。理论分析及实验结果表明，该机制能在合理的时间和空间消耗下对用户位置隐私信息进行有效保护。%As a new type of wireless sensor network system, people-centric sensing network plays a key role in many areas. However, location privacy information is easily leaked due to the semantics-aware threat. Therefore, designing a privacy-reserving scheme which is suitable for people-centric sensing network and can resist the semantics-aware threat is an important and challenging work. Firstly, this paper combines the semantics attributes of geographic position and the obfuscation space technology to reduce the probability of an adversary using the semantics-aware threat to get the user's position privacy information. Then, a new obfuscation space selection algorithm is proposed by using BP neural network technology, which can obtain the appropriate obfuscation space in case of user not transmitting its own actual position information, in order to prevent an adversary to acquire the user’s location privacy information by using the non trusted third party such as obfuscation space generator and server. The theoretical analysis and experimental results show that the scheme can protect the user's location privacy information in a reasonable amount of time and space consumption.