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1. Daily activity locations k-anonymity for the evaluation of disclosure risk of individual GPS datasets

   https://doi.org/10.1186/s12942-020-00201-9  

   Wang, Jue; Kwan, Mei-Po (December 2020, International Journal of Health Geographics)

   null (Ed.)

   Abstract Background Personal privacy is a significant concern in the era of big data. In the field of health geography, personal health data are collected with geographic location information which may increase disclosure risk and threaten personal geoprivacy. Geomasking is used to protect individuals’ geoprivacy by masking the geographic location information, and spatial k-anonymity is widely used to measure the disclosure risk after geomasking is applied. With the emergence of individual GPS trajectory datasets that contains large volumes of confidential geospatial information, disclosure risk can no longer be comprehensively assessed by the spatial k-anonymity method. Methods This study proposes and develops daily activity locations (DAL) k-anonymity as a new method for evaluating the disclosure risk of GPS data. Instead of calculating disclosure risk based on only one geographic location (e.g., home) of an individual, the new DAL k-anonymity is a composite evaluation of disclosure risk based on all activity locations of an individual and the time he/she spends at each location abstracted from GPS datasets. With a simulated individual GPS dataset, we present case studies of applying DAL k-anonymity in various scenarios to investigate its performance. The results of applying DAL k-anonymity are also compared with those obtained with spatial k-anonymity under these scenarios. Results The results of this study indicate that DAL k-anonymity provides a better estimation of the disclosure risk than does spatial k-anonymity. In various case-study scenarios of individual GPS data, DAL k-anonymity provides a more effective method for evaluating the disclosure risk by considering the probability of re-identifying an individual’s home and all the other daily activity locations. Conclusions This new method provides a quantitative means for understanding the disclosure risk of sharing or publishing GPS data. It also helps shed new light on the development of new geomasking methods for GPS datasets. Ultimately, the findings of this study will help to protect individual geoprivacy while benefiting the research community by promoting and facilitating geospatial data sharing. 

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2. Quantifying location privacy in permissioned blockchain-based internet of things (IoT)

   https://doi.org/10.1145/3360774.3360800  

   Shahid, Abdur R.; Pissinou, Niki; Njilla, Laurent; Alemany, Sheila; Imteaj, Ahmed; Makki, Kia; Aguilar, Edwin (November 2019, MobiQuitous '19: Proceedings of the 16th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services)

   Vincent Poor and Zhu Han (Ed.)

   Recently, blockchain has received much attention from the mobility-centric Internet of Things (IoT). It is deemed the key to ensuring the built-in integrity of information and security of immutability by design in the peer-to-peer network (P2P) of mobile devices. In a permissioned blockchain, the authority of the system has control over the identities of its users. Such information can allow an ill-intentioned authority to map identities with their spatiotemporal data, which undermines the location privacy of a mobile user. In this paper, we study the location privacy preservation problem in the context of permissioned blockchain-based IoT systems under three conditions. First, the authority of the blockchain holds the public and private key distribution task in the system. Second, there exists a spatiotemporal correlation between consecutive location-based transactions. Third, users communicate with each other through short-range communication technologies such that it constitutes a proof of location (PoL) on their actual locations. We show that, in a permissioned blockchain with an authority and a presence of a PoL, existing approaches cannot be applied using a plug-and-play approach to protect location privacy. In this context, we propose BlockPriv, an obfuscation technique that quantifies, both theoretically and experimentally, the relationship between privacy and utility in order to dynamically protect the privacy of sensitive locations in the permissioned blockchain. 

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3. Differentially Private Formation Control

   https://doi.org/10.1109/CDC42340.2020.9303817  

   Hawkins, Calvin; Hale, Matthew (December 2020, Proceedings of the 2020 59th IEEE Conference on Decision and Control (CDC))

   null (Ed.)

   As multi-agent systems proliferate, there is in-creasing demand for coordination protocols that protect agents’ sensitive information while allowing them to collaborate. To help address this need, this paper presents a differentially private formation control framework. Agents’ state trajectories are protected using differential privacy, which is a statistical notion of privacy that protects data by adding noise to it. We provide a private formation control implementation and analyze the impact of privacy upon the system. Specifically, we quantify tradeoffs between privacy level, system performance, and connectedness of the network’s communication topology. These tradeoffs are used to develop guidelines for calibrating privacy in terms of control theoretic quantities, such as steady-state error, without requiring in-depth knowledge of differential privacy. Additional guidelines are also developed for treating privacy levels and network topologies as design parameters to tune the network’s performance. Simulation results illustrate these tradeoffs and show that strict privacy is inherently compatible with strong system performance. 

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4. Location Leakage in Federated Signal Maps

   https://doi.org/10.1109/TMC.2023.3332034  

   Bakopoulou, Evita; Yang, Mengwei; Zhang, Jiang; Psounis, Konstantinos; Markopoulou, Athina (June 2024, IEEE Transactions on Mobile Computing)

   We consider the problem of predicting cellular network performance (signal maps) from measurements collected by several mobile devices. We formulate the problem within the online federated learning framework: (i) federated learning (FL) enables users to collaboratively train a model, while keeping their training data on their devices; (ii) measurements are collected as users move around over time and are used for local training in an online fashion. We consider an honest-but-curious server, who observes the updates from target users participating in FL and infers their location using a deep leakage from gradients (DLG) type of attack, originally developed to reconstruct training data of DNN image classifiers. We make the key observation that a DLG attack, applied to our setting, infers the average location of a batch of local data, and can thus be used to reconstruct the target users' trajectory at a coarse granularity. We build on this observation to protect location privacy, in our setting, by revisiting and designing mechanisms within the federated learning framework including: tuning the FL parameters for averaging, curating local batches so as to mislead the DLG attacker, and aggregating across multiple users with different trajectories. We evaluate the performance of our algorithms through both analysis and simulation based on real-world mobile datasets, and we show that they achieve a good privacy-utility tradeoff. 

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5. Preserving Incumbent User’s Location Privacy Against Environmental Sensing Capability

   https://doi.org/10.1109/DySPAN.2019.8935713  

   Lin, Yousi; Ye, Yuxian; Yang, Yaling (November 2019, 2019 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN))

   In dynamic spectrum access (DSA), Environmental Sensing Capability (ESC) systems are implemented to detect the incumbent users' (IU) activities for protecting them from secondary users' (SU) interference as well as maximizing secondary spectrum usage. However, IU location information is often highly sensitive and hence it is preferable to hide its true location under the detection of ESCs. In this paper, we design novel schemes to preserve both static and moving IU's location information by adjusting IU's radiation pattern and transmit power. We first formulate IU privacy protection problem for static IU. Due to the intractable nature of this problem, we propose a heuristic approach based on sampling. We also formulate the privacy protection problem for moving IUs, in which two cases are analyzed: (1) protect IU's moving traces; (2) protect its real-time current location information. Our analysis provides insightful advice for IU to preserve its location privacy against ESCs. Simulation results show that our approach provides great protection for IU's location privacy. 

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