Abstract
With the booming development of technology and location tracking, location-based services (LBSs) are widely used in mobile phone applications. According to LBS, we can know the location information of relatives at any time, search the surrounding hospitals, restaurants and so on. Moreover, the police can use the LBS to catch escaped prisoners. Generally speaking, LBS brings a lot of convenience to our life and keep our lives safe. However, there are also hidden threats to LBS. Many criminals use LBS to steal location information to track or analyze the preferences of their victims to promote their products. This kind of harassment will affect people’s normal life. It may threaten the location privacy of users. What more serious is it may damage the life and property of users who use an unsafe location system on the internet. To protect the location privacy of users more efficiently, we come up with a new model that includes the traditional model of location privacy and generative-adversarial net (GANs). As we all know generative-adversarial net (GANs) can generate fake data that is closed to real data. Therefore, we use the generative-adversarial net (GANs) to generate a cloaking region (CR) (as same as real location and attacker cannot perceive its false) so that it protects users’ location privacy more efficiently. In our simulation, we generate the cloaking region (CR) in generative-adversarial net (GANs) by the eigenvalues of the initial location. Hence, the cloaking region (CR) we generated is harder to detect than others. But we cannot get details of the eigenvalues and it is bounded to the range of cloaking region (CR) now. We hope we can deal with these problems someday in the future.
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Wang, W. et al. (2020). A Novel Location Privacy Protection Scheme with Generative Adversarial Network. In: Tian, Y., Ma, T., Khan, M. (eds) Big Data and Security. ICBDS 2019. Communications in Computer and Information Science, vol 1210. Springer, Singapore. https://doi.org/10.1007/978-981-15-7530-3_2
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DOI: https://doi.org/10.1007/978-981-15-7530-3_2
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