Simulation-based analysis of threats to location privacy in fog computing

In fog computing, end devices can benefit from low-latency access to computing capacity provided by nearby fog nodes. However, an adversary controlling some fog nodes may infer information about the location of end devices that engage with these fog nodes. The goal of this paper is to analyze the severity of this threat to location privacy. We analyze how precise the information leaked by fog nodes about the location of end devices may be, and how this depends on the ratio of compromised fog nodes and on the adversary's background knowledge. We present the simulator LocPrivFogSim, which extends the existing fog computing simulator MobFogSim with the constructs necessary to model location privacy threats. The findings from preliminary simulations of various attack scenarios show that an attacker controlling even a modest ratio of the fog nodes may be able to infer precise information about the location and trajectory of end devices.

[1]  Walid G. Aref,et al.  Casper*: Query processing for location services without compromising privacy , 2006, TODS.

[2]  Enzo Mingozzi,et al.  MobFogSim: Simulation of mobility and migration for fog computing , 2020, Simul. Model. Pract. Theory.

[3]  Yonghong Chen,et al.  Trajectory Privacy Preservation Based on a Fog Structure for Cloud Location Services , 2017, IEEE Access.

[4]  Zoltán Ádám Mann,et al.  Secure software placement and configuration , 2020, Future Gener. Comput. Syst..

[5]  Ling Liu,et al.  Protecting Location Privacy with Personalized k-Anonymity: Architecture and Algorithms , 2008, IEEE Transactions on Mobile Computing.

[6]  Marco Gruteser,et al.  On the Anonymity of Periodic Location Samples , 2005, SPC.

[7]  John Krumm,et al.  A survey of computational location privacy , 2009, Personal and Ubiquitous Computing.

[8]  Junhua Wu,et al.  Trajectory Privacy Protection Method Based on Location Service in Fog Computing , 2018, IIKI.

[9]  Qi Li,et al.  Location Privacy-Preserving Method Based on Historical Proximity Location , 2020, Wirel. Commun. Mob. Comput..

[10]  Donggang Liu,et al.  Protecting Location Privacy in Sensor Networks against a Global Eavesdropper , 2012, IEEE Transactions on Mobile Computing.

[11]  Jason P. Jue,et al.  All One Needs to Know about Fog Computing and Related Edge Computing Paradigms , 2019 .

[12]  Helen J. Wang,et al.  Preserving location privacy in wireless lans , 2007, MobiSys '07.

[13]  Sabrina De Capitani di Vimercati,et al.  An Obfuscation-Based Approach for Protecting Location Privacy , 2011, IEEE Transactions on Dependable and Secure Computing.

[14]  Jianliang Xu,et al.  Protecting Location Privacy against Location-Dependent Attacks in Mobile Services , 2012, IEEE Transactions on Knowledge and Data Engineering.

[15]  Rajkumar Buyya,et al.  Fog Computing: Helping the Internet of Things Realize Its Potential , 2016, Computer.

[16]  Radha Poovendran,et al.  Swing & swap: user-centric approaches towards maximizing location privacy , 2006, WPES '06.

[17]  Qi He,et al.  The quest for personal control over mobile location privacy , 2004, IEEE Communications Magazine.

[18]  Helen J. Wang,et al.  A Framework for Location Privacy in Wireless Networks , 2005 .

[19]  Frank Dürr,et al.  A classification of location privacy attacks and approaches , 2012, Personal and Ubiquitous Computing.

[20]  Rajkumar Buyya,et al.  iFogSim: A toolkit for modeling and simulation of resource management techniques in the Internet of Things, Edge and Fog computing environments , 2016, Softw. Pract. Exp..

[21]  Arwa Alrawais,et al.  Fog Computing for the Internet of Things: Security and Privacy Issues , 2017, IEEE Internet Computing.

[22]  Zoltán Ádám Mann,et al.  Comparison of simulators for fog computing , 2020, SAC.

[23]  Jean-Yves Le Boudec,et al.  Quantifying Location Privacy , 2011, 2011 IEEE Symposium on Security and Privacy.

[24]  Zoltán Ádám Mann,et al.  Optimization Problems in Fog and Edge Computing , 2019, Fog and Edge Computing.

[25]  Qun Li,et al.  Security and Privacy Issues of Fog Computing: A Survey , 2015, WASA.

[26]  Marco Gruteser,et al.  Enhancing Location Privacy in Wireless LAN Through Disposable Interface Identifiers: A Quantitative Analysis , 2003, WMASH '03.

[27]  Zhili Sun,et al.  Security and Privacy in Location-Based Services for Vehicular and Mobile Communications: An Overview, Challenges, and Countermeasures , 2018, IEEE Internet of Things Journal.