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2021-07-27
Lu, Tao, Xu, Hongyun, Tian, Kai, Tian, Cenxi, Jiang, Rui.  2020.  Semantic Location Privacy Protection Algorithm Based on Edge Cluster Graph. 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). :1304–1309.
With the development of positioning technology and the popularity of mobile devices, location-based services have been widely deployed. To use the services, users must provide the server accurate location information, during which the attacker tends to infer sensitive information from intercepting queries. In this paper, we model the road network as an edge cluster graph with its location semantics considered. Then, we propose the Circle First Structure Optimization (CFSO) algorithm which generates an anonymous set by adding optimal adjacent locations. Furthermore, we introduce controllable randomness and propose the Attack-Resilient (AR) algorithm to enhance the anti-attack ability. Meanwhile, to reduce the system overhead, our algorithms build the anonymous set quickly and take the structure of the anonymous set into account. Finally, we conduct experiments on a real map and the results demonstrate a higher anonymity success rate and a stronger anti-attack capability with less system overhead.
2021-07-08
Abdo, Mahmoud A., Abdel-Hamid, Ayman A., Elzouka, Hesham A..  2020.  A Cloud-based Mobile Healthcare Monitoring Framework with Location Privacy Preservation. 2020 International Conference on Innovation and Intelligence for Informatics, Computing and Technologies (3ICT). :1—8.
Nowadays, ubiquitous healthcare monitoring applications are becoming a necessity. In a pervasive smart healthcare system, the user's location information is always transmitted periodically to healthcare providers to increase the quality of the service provided to the user. However, revealing the user's location will affect the user's privacy. This paper presents a novel cloud-based secure location privacy-preserving mobile healthcare framework with decision-making capabilities. A user's vital signs are sensed possibly through a wearable healthcare device and transmitted to a cloud server for securely storing user's data, processing, and decision making. The proposed framework integrates a number of features such as machine learning (ML) for classifying a user's health state, and crowdsensing for collecting information about a person's privacy preferences for possible locations and applying such information to a user who did not set his privacy preferences. In addition to location privacy preservation methods (LPPM) such as obfuscation, perturbation and encryption to protect the location of the user and provide a secure monitoring framework. The proposed framework detects clear emergency cases and quickly decides about sending a help message to a healthcare provider before sending data to the cloud server. To validate the efficiency of the proposed framework, a prototype is developed and tested. The obtained results from the proposed prototype prove its feasibility and utility. Compared to the state of art, the proposed framework offers an adaptive context-based decision for location sharing privacy and controlling the trade-off between location privacy and service utility.
2020-11-02
Ma, Y., Bai, X..  2019.  Comparison of Location Privacy Protection Schemes in VANETs. 2019 12th International Symposium on Computational Intelligence and Design (ISCID). 2:79–83.
Vehicular Ad-hoc Networks (VANETs) is a traditional mobile ad hoc network (MANET) used on traffic roads and it is a special mobile ad hoc network. As an intelligent transportation system, VANETs can solve driving safety and provide value-added services. Therefore, the application of VANETs can improve the safety and efficiency of road traffic. Location services are in a crucial position for the development of VANETs. VANETs has the characteristics of open access and wireless communication. Malicious node attacks may lead to the leakage of user privacy in VANETs, thus seriously affecting the use of VANETs. Therefore, the location privacy issue of VANETs cannot be ignored. This paper classifies the attack methods in VANETs, and summarizes and compares the location privacy protection techniques proposed in the existing research.
2020-10-26
Dagelić, Ante, Perković, Toni, Čagalj, Mario.  2019.  Location Privacy and Changes in WiFi Probe Request Based Connection Protocols Usage Through Years. 2019 4th International Conference on Smart and Sustainable Technologies (SpliTech). :1–5.
Location privacy is one of most frequently discussed terms in the mobile devices security breaches and data leaks. With the expected growth of the number of IoT devices, which is 20 billions by 2020., location privacy issues will be further brought to focus. In this paper we give an overview of location privacy implications in wireless networks, mainly focusing on user's Preferred Network List (list of previously used WiFi Access Points) contained within WiFi Probe Request packets. We will showcase the existing work and suggest interesting topics for future work. A chronological overview of sensitive location data we collected on a musical festival in years 2014, 2015, 2017 and 2018 is provided. We conclude that using passive WiFi monitoring scans produces different results through years, with a significant increase in the usage of a more secure Broadcast Probe Request packets and MAC address randomizations by the smartphone operating systems.
Zhou, Liming, Shan, Yingzi.  2019.  Multi-branch Source Location Privacy Protection Scheme Based on Random Walk in WSNs. 2019 IEEE 4th International Conference on Cloud Computing and Big Data Analysis (ICCCBDA). :543–547.
In many applications, source nodes send the sensing information of the monitored objects and the sinks receive the transmitted data. Considering the limited resources of sensor nodes, location privacy preservation becomes an important issue. Although many schemes are proposed to preserve source or sink location security, few schemes can preserve the location security of source nodes and sinks. In order to solve this problem, we propose a novel of multi-branch source location privacy protection method based on random walk. This method hides the location of real source nodes by setting multiple proxy sources. And multiple neighbors are randomly selected by the real source node as receivers until a proxy source receives the packet. In addition, the proxy source is chosen randomly, which can prevent the attacker from obtaining the location-related data of the real source node. At the same time, the scheme sets up a branch interference area around the base station to interfere with the adversary by increasing routing branches. Simulation results describe that our scheme can efficiently protect source and sink location privacy, reduce the communication overhead, and prolong the network lifetime.
DaSilva, Gianni, Loud, Vincent, Salazar, Ana, Soto, Jeff, Elleithy, Abdelrahman.  2019.  Context-Oriented Privacy Protection in Wireless Sensor Networks. 2019 IEEE Long Island Systems, Applications and Technology Conference (LISAT). :1–4.
As more devices become connected to the internet and new technologies emerge to connect them, security must keep up to protect data during transmission and at rest. Several instances of security breaches have forced many companies to investigate the effectiveness of their security measures. In this paper, we discuss different methodologies for protecting data as it relates to wireless sensor networks (WSNs). Data collected from these sensors range in type from location data of an individual to surveillance for military applications. We propose a solution that protects the location of the base station and the nodes while transmitting data.
George, Chinnu Mary, Luke Babu, Sharon.  2019.  A Scalable Correlation Clustering strategy in Location Privacy for Wireless Sensor Networks against a Universal Adversary. 2019 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE). :1–3.
Wireless network sensors are outsized number of pocket sized sensors deployed in the area under surveillance. The sensor network is very sensitive to unattended and remote Environment with a wide variety of applications in the agriculture, health, industry there a lot of challenges being faced with respect to the energy, mobility, security. The paper presents with regard to the context based surrounding information which has location privacy to the source node against an adversary who sees the network at a whole so a correlation strategy is proposed for providing the privacy.
Li, Qingyuan, Wu, Hao, Liu, Lei, Pan, Bin, Dong, Lan.  2018.  A Group based Dynamic Mix Zone Scheme for Location Privacy Preservation in VANETs. 2018 Third International Conference on Security of Smart Cities, Industrial Control System and Communications (SSIC). :1–5.
Modern vehicles are equipped with wireless communication technologies, allowing them to communicate with each other. Through Dedicated Short Range Communication (DSRC), vehicles periodically broadcast beacons messages for safety applications, which gives rise to disclosure of location privacy. A way to protect vehicles location privacy is to have their pseudonyms changed frequently. With restrict to limited resources (such as computation and storage), we propose a group based dynamic mix zone scheme, in which vehicles form a group when their pseudonyms are close to expire. Simulation results confirm that the proposed scheme can protect location privacy and alleviate the storage burden.
Zhang, Kewang, Zahng, Qiong.  2018.  Preserve Location Privacy for Cyber-Physical Systems with Addresses Hashing at Data Link Layer. 2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS). :1028–1032.
Due to their low complexity and robustness in nature, wireless sensor networks are a key component in cyber-physical system. The integration of wireless sensor network in cyber-physical system provides immense benefits in distributed controlled environment. However, the open nature of the wireless medium makes resource-constrained WSN vulnerable to unauthorized interception and detection. Privacy is becoming one of the major issues that jeopardize the successful deployment of WSN. In this paper, we propose a scheme named HASHA to provide location privacy. Different from previous approaches, HASHA protect nodes' location privacy at data link layer. It is well known that payload at data link layer frame is well protected through cryptosystem, but addresses at data link layer leaves unprotected. The adversaries can identify nodes in the network easily by capturing frames and check the source and destination addresses. If both addresses are well protected and unknown to the adversaries, they cannot identify nodes of the targeted networks, rendering it very difficult to launch traffic analysis and locate subjects. Simulation and analytical results demonstrate that our scheme provides stronger privacy protection and requires much less energy.
2020-09-28
Butun, Ismail, Österberg, Patrik, Gidlund, Mikael.  2019.  Preserving Location Privacy in Cyber-Physical Systems. 2019 IEEE Conference on Communications and Network Security (CNS). :1–6.
The trending technological research platform is Internet of Things (IoT)and most probably it will stay that way for a while. One of the main application areas of IoT is Cyber-Physical Systems (CPSs), in which IoT devices can be leveraged as actuators and sensors in accordance with the system needs. The public acceptance and adoption of CPS services and applications will create a huge amount of privacy issues related to the processing, storage and disclosure of the user location information. As a remedy, our paper proposes a methodology to provide location privacy for the users of CPSs. Our proposal takes advantage of concepts such as mix-zone, context-awareness, and location-obfuscation. According to our best knowledge, the proposed methodology is the first privacy-preserving location service for CPSs that offers adaptable privacy levels related to the current context of the user.
Oya, Simon, Troncoso, Carmela, Pèrez-Gonzàlez, Fernando.  2019.  Rethinking Location Privacy for Unknown Mobility Behaviors. 2019 IEEE European Symposium on Security and Privacy (EuroS P). :416–431.
Location Privacy-Preserving Mechanisms (LPPMs) in the literature largely consider that users' data available for training wholly characterizes their mobility patterns. Thus, they hardwire this information in their designs and evaluate their privacy properties with these same data. In this paper, we aim to understand the impact of this decision on the level of privacy these LPPMs may offer in real life when the users' mobility data may be different from the data used in the design phase. Our results show that, in many cases, training data does not capture users' behavior accurately and, thus, the level of privacy provided by the LPPM is often overestimated. To address this gap between theory and practice, we propose to use blank-slate models for LPPM design. Contrary to the hardwired approach, that assumes known users' behavior, blank-slate models learn the users' behavior from the queries to the service provider. We leverage this blank-slate approach to develop a new family of LPPMs, that we call Profile Estimation-Based LPPMs. Using real data, we empirically show that our proposal outperforms optimal state-of-the-art mechanisms designed on sporadic hardwired models. On non-sporadic location privacy scenarios, our method is only better if the usage of the location privacy service is not continuous. It is our hope that eliminating the need to bootstrap the mechanisms with training data and ensuring that the mechanisms are lightweight and easy to compute help fostering the integration of location privacy protections in deployed systems.
2020-08-13
Yu, Lili, Su, Xiaoguang, Zhang, Lei.  2019.  Collaboration-Based Location Privacy Protection Method. 2019 IEEE 2nd International Conference on Electronics Technology (ICET). :639—643.
In the privacy protection method based on user collaboration, all participants and collaborators must share the maximum anonymity value set in the anonymous group. No user can get better quality of service by reducing the anonymity requirement. In this paper, a privacy protection algorithm random-QBE, which divides query information into blocks and exchanges randomly, is proposed. Through this method, personalized anonymity, query diversity and location anonymity in user cooperative privacy protection can be realized. And through multi-hop communication between collaborative users, this method can also satisfy the randomness of anonymous location, so that the location of the applicant is no longer located in the center of the anonymous group, which further increases the ability of privacy protection. Experiments show that the algorithm can complete the processing in a relatively short time and is suitable for deployment in real environment to protect user's location privacy.
2020-04-20
To, Hien, Shahabi, Cyrus, Xiong, Li.  2018.  Privacy-Preserving Online Task Assignment in Spatial Crowdsourcing with Untrusted Server. 2018 IEEE 34th International Conference on Data Engineering (ICDE). :833–844.
With spatial crowdsourcing (SC), requesters outsource their spatiotemporal tasks (tasks associated with location and time) to a set of workers, who will perform the tasks by physically traveling to the tasks' locations. However, current solutions require the locations of the workers and/or the tasks to be disclosed to untrusted parties (SC server) for effective assignments of tasks to workers. In this paper we propose a framework for assigning tasks to workers in an online manner without compromising the location privacy of workers and tasks. We perturb the locations of both tasks and workers based on geo-indistinguishability and then devise techniques to quantify the probability of reachability between a task and a worker, given their perturbed locations. We investigate both analytical and empirical models for quantifying the worker-task pair reachability and propose task assignment strategies that strike a balance among various metrics such as the number of completed tasks, worker travel distance and system overhead. Extensive experiments on real-world datasets show that our proposed techniques result in minimal disclosure of task locations and no disclosure of worker locations without significantly sacrificing the total number of assigned tasks.
To, Hien, Shahabi, Cyrus, Xiong, Li.  2018.  Privacy-Preserving Online Task Assignment in Spatial Crowdsourcing with Untrusted Server. 2018 IEEE 34th International Conference on Data Engineering (ICDE). :833–844.
With spatial crowdsourcing (SC), requesters outsource their spatiotemporal tasks (tasks associated with location and time) to a set of workers, who will perform the tasks by physically traveling to the tasks' locations. However, current solutions require the locations of the workers and/or the tasks to be disclosed to untrusted parties (SC server) for effective assignments of tasks to workers. In this paper we propose a framework for assigning tasks to workers in an online manner without compromising the location privacy of workers and tasks. We perturb the locations of both tasks and workers based on geo-indistinguishability and then devise techniques to quantify the probability of reachability between a task and a worker, given their perturbed locations. We investigate both analytical and empirical models for quantifying the worker-task pair reachability and propose task assignment strategies that strike a balance among various metrics such as the number of completed tasks, worker travel distance and system overhead. Extensive experiments on real-world datasets show that our proposed techniques result in minimal disclosure of task locations and no disclosure of worker locations without significantly sacrificing the total number of assigned tasks.
Sule, Rupali, Chaudhari, Sangita.  2018.  Preserving Location Privacy in Geosocial Applications using Error Based Transformation. 2018 International Conference on Smart City and Emerging Technology (ICSCET). :1–4.
Geo-social applications deal with constantly sharing user's current geographic information in terms of location (Latitude and Longitude). Such application can be used by many people to get information about their surrounding with the help of their friend's locations and their recommendations. But without any privacy protection, these systems can be easily misused by tracking the users. We are proposing Error Based Transformation (ERB) approach for location transformation which provides significantly improved location privacy without adding uncertainty in to query results or relying on strong assumptions about server security. The key insight is to apply secure user-specific, distance-preserving coordinate transformations to all location data shared with the server. Only the friends of a user can get exact co-ordinates by applying inverse transformation with secret key shared with them. Servers can evaluate all location queries correctly on transformed data. ERB privacy mechanism guarantee that servers are unable to see or infer actual location data from the transformed data. ERB privacy mechanism is successful against a powerful adversary model where prototype measurements used to show that it provides with very little performance overhead making it suitable for today's mobile device.
Sule, Rupali, Chaudhari, Sangita.  2018.  Preserving Location Privacy in Geosocial Applications using Error Based Transformation. 2018 International Conference on Smart City and Emerging Technology (ICSCET). :1–4.
Geo-social applications deal with constantly sharing user's current geographic information in terms of location (Latitude and Longitude). Such application can be used by many people to get information about their surrounding with the help of their friend's locations and their recommendations. But without any privacy protection, these systems can be easily misused by tracking the users. We are proposing Error Based Transformation (ERB) approach for location transformation which provides significantly improved location privacy without adding uncertainty in to query results or relying on strong assumptions about server security. The key insight is to apply secure user-specific, distance-preserving coordinate transformations to all location data shared with the server. Only the friends of a user can get exact co-ordinates by applying inverse transformation with secret key shared with them. Servers can evaluate all location queries correctly on transformed data. ERB privacy mechanism guarantee that servers are unable to see or infer actual location data from the transformed data. ERB privacy mechanism is successful against a powerful adversary model where prototype measurements used to show that it provides with very little performance overhead making it suitable for today's mobile device.
2020-03-02
Arifeen, Md Murshedul, Islam, Al Amin, Rahman, Md Mustafizur, Taher, Kazi Abu, Islam, Md.Maynul, Kaiser, M Shamim.  2019.  ANFIS based Trust Management Model to Enhance Location Privacy in Underwater Wireless Sensor Networks. 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE). :1–6.
Trust management is a promising alternative solution to different complex security algorithms for Underwater Wireless Sensor Networks (UWSN) applications due to its several resource constraint behaviour. In this work, we have proposed a trust management model to improve location privacy of the UWSN. Adaptive Neuro Fuzzy Inference System (ANFIS) has been exploited to evaluate trustworthiness of a sensor node. Also Markov Decision Process (MDP) has been considered. At each state of the MDP, a sensor node evaluates trust behaviour of forwarding node utilizing the FIS learning rules and selects a trusted node. Simulation has been conducted in MATLAB and simulation results show that the detection accuracy of trustworthiness is 91.2% which is greater than Knowledge Discovery and Data Mining (KDD) 99 intrusion detection based dataset. So, in our model 91.2% trustworthiness is necessary to be a trusted node otherwise it will be treated as a malicious or compromised node. Our proposed model can successfully eliminate the possibility of occurring any compromised or malicious node in the network.
2019-12-16
Zhou, Liming, Shan, Yingzi, Chen, Xiaopan.  2019.  An Anonymous Routing Scheme for Preserving Location Privacy in Wireless Sensor Networks. 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). :262-265.

Wireless sensor networks consist of various sensors that are deployed to monitor the physical world. And many existing security schemes use traditional cryptography theory to protect message content and contextual information. However, we are concerned about location security of nodes. In this paper, we propose an anonymous routing strategy for preserving location privacy (ARPLP), which sets a proxy source node to hide the location of real source node. And the real source node randomly selects several neighbors as receivers until the packets are transmitted to the proxy source. And the proxy source is randomly selected so that the adversary finds it difficult to obtain the location information of the real source node. Meanwhile, our scheme sets a branch area around the sink, which can disturb the adversary by increasing the routing branch. According to the analysis and simulation experiments, our scheme can reduce traffic consumption and communication delay, and improve the security of source node and base station.

Cerf, Sophie, Robu, Bogdan, Marchand, Nicolas, Mokhtar, Sonia Ben, Bouchenak, Sara.  2018.  A Control-Theoretic Approach for Location Privacy in Mobile Applications. 2018 IEEE Conference on Control Technology and Applications (CCTA). :1488-1493.

The prevalent use of mobile applications using location information to improve the quality of their service has arisen privacy issues, particularly regarding the extraction of user's points on interest. Many studies in the literature focus on presenting algorithms that allow to protect the user of such applications. However, these solutions often require a high level of expertise to be understood and tuned properly. In this paper, the first control-based approach of this problem is presented. The protection algorithm is considered as the ``physical'' plant and its parameters as control signals that enable to guarantee privacy despite user's mobility pattern. The following of the paper presents the first control formulation of POI-related privacy measure, as well as dynamic modeling and a simple yet efficient PI control strategy. The evaluation using simulated mobility records shows the relevance and efficiency of the presented approach.

2019-03-06
Gursoy, Mehmet Emre, Liu, Ling, Truex, Stacey, Yu, Lei, Wei, Wenqi.  2018.  Utility-Aware Synthesis of Differentially Private and Attack-Resilient Location Traces. Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security. :196-211.
As mobile devices and location-based services become increasingly ubiquitous, the privacy of mobile users' location traces continues to be a major concern. Traditional privacy solutions rely on perturbing each position in a user's trace and replacing it with a fake location. However, recent studies have shown that such point-based perturbation of locations is susceptible to inference attacks and suffers from serious utility losses, because it disregards the moving trajectory and continuity in full location traces. In this paper, we argue that privacy-preserving synthesis of complete location traces can be an effective solution to this problem. We present AdaTrace, a scalable location trace synthesizer with three novel features: provable statistical privacy, deterministic attack resilience, and strong utility preservation. AdaTrace builds a generative model from a given set of real traces through a four-phase synthesis process consisting of feature extraction, synopsis learning, privacy and utility preserving noise injection, and generation of differentially private synthetic location traces. The output traces crafted by AdaTrace preserve utility-critical information existing in real traces, and are robust against known location trace attacks. We validate the effectiveness of AdaTrace by comparing it with three state of the art approaches (ngram, DPT, and SGLT) using real location trace datasets (Geolife and Taxi) as well as a simulated dataset of 50,000 vehicles in Oldenburg, Germany. AdaTrace offers up to 3-fold improvement in trajectory utility, and is orders of magnitude faster than previous work, while preserving differential privacy and attack resilience.
2019-02-18
Wang, G., Wang, B., Wang, T., Nika, A., Zheng, H., Zhao, B. Y..  2018.  Ghost Riders: Sybil Attacks on Crowdsourced Mobile Mapping Services. IEEE/ACM Transactions on Networking. 26:1123–1136.
Real-time crowdsourced maps, such as Waze provide timely updates on traffic, congestion, accidents, and points of interest. In this paper, we demonstrate how lack of strong location authentication allows creation of software-based Sybil devices that expose crowdsourced map systems to a variety of security and privacy attacks. Our experiments show that a single Sybil device with limited resources can cause havoc on Waze, reporting false congestion and accidents and automatically rerouting user traffic. More importantly, we describe techniques to generate Sybil devices at scale, creating armies of virtual vehicles capable of remotely tracking precise movements for large user populations while avoiding detection. To defend against Sybil devices, we propose a new approach based on co-location edges, authenticated records that attest to the one-time physical co-location of a pair of devices. Over time, co-location edges combine to form large proximity graphs that attest to physical interactions between devices, allowing scalable detection of virtual vehicles. We demonstrate the efficacy of this approach using large-scale simulations, and how they can be used to dramatically reduce the impact of the attacks. We have informed Waze/Google team of our research findings. Currently, we are in active collaboration with Waze team to improve the security and privacy of their system.
2018-12-03
Catania, E., Corte, A. La.  2018.  Location Privacy in Virtual Cell-Equipped Ultra-Dense Networks. 2018 9th IFIP International Conference on New Technologies, Mobility and Security (NTMS). :1–4.

Ultra-dense Networks are attracting significant interest due to their ability to provide the next generation 5G cellular networks with a high data rate, low delay, and seamless coverage. Several factors, such as interferences, energy constraints, and backhaul bottlenecks may limit wireless networks densification. In this paper, we study the effect of mobile node densification, access node densification, and their aggregation into virtual entities, referred to as virtual cells, on location privacy. Simulations show that the number of tracked mobile nodes might be statistically reduced up to 10 percent by implementing virtual cells. Moreover, experiments highlight that success of tracking attacks has an inverse relationship to the number of moving nodes. The present paper is a preliminary attempt to analyse the effectiveness of cell virtualization to mitigate location privacy threats in ultra-dense networks.

2018-11-28
Agadakos, Ioannis, Polakis, Jason, Portokalidis, Georgios.  2017.  Techu: Open and Privacy-Preserving Crowdsourced GPS for the Masses. Proceedings of the 15th Annual International Conference on Mobile Systems, Applications, and Services. :475–487.

The proliferation of mobile devices, equipped with numerous sensors and Internet connectivity, has laid the foundation for the emergence of a diverse set of crowdsourcing services. By leveraging the multitude, geographical dispersion, and technical abilities of smartphones, these services tackle challenging tasks by harnessing the power of the crowd. One such service, Crowd GPS, has gained traction in the industry and research community alike, materializing as a class of systems that track lost objects or individuals (e.g., children or elders). While these systems can have significant impact, they suffer from major privacy threats. In this paper, we highlight the inherent risks to users from the centralized designs adopted by such services and demonstrate how adversaries can trivially misuse one of the most popular crowd GPS services to track their users. As an alternative, we present Techu, a privacy-preserving crowd GPS service for tracking Bluetooth tags. Our architecture follows a hybrid decentralized approach, where an untrusted server acts as a bulletin board that collects reports of tags observed by the crowd, while observers store the location information locally and only disclose it upon proof of ownership of the tag. Techu does not require user authentication, allowing users to remain anonymous. As no user authentication is required and cloud messaging queues are leveraged for communication between users, users remain anonymous. Our security analysis highlights the privacy offered by Techu, and details how our design prevents adversaries from tracking or identifying users. Finally, our experimental evaluation demonstrates that Techu has negligible impact on power consumption, and achieves superior effectiveness to previously proposed systems while offering stronger privacy guarantees.

2018-09-05
Li, C., Palanisamy, B., Joshi, J..  2017.  Differentially Private Trajectory Analysis for Points-of-Interest Recommendation. 2017 IEEE International Congress on Big Data (BigData Congress). :49–56.

Ubiquitous deployment of low-cost mobile positioning devices and the widespread use of high-speed wireless networks enable massive collection of large-scale trajectory data of individuals moving on road networks. Trajectory data mining finds numerous applications including understanding users' historical travel preferences and recommending places of interest to new visitors. Privacy-preserving trajectory mining is an important and challenging problem as exposure of sensitive location information in the trajectories can directly invade the location privacy of the users associated with the trajectories. In this paper, we propose a differentially private trajectory analysis algorithm for points-of-interest recommendation to users that aims at maximizing the accuracy of the recommendation results while protecting the privacy of the exposed trajectories with differential privacy guarantees. Our algorithm first transforms the raw trajectory dataset into a bipartite graph with nodes representing the users and the points-of-interest and the edges representing the visits made by the users to the locations, and then extracts the association matrix representing the bipartite graph to inject carefully calibrated noise to meet έ-differential privacy guarantees. A post-processing of the perturbed association matrix is performed to suppress noise prior to performing a Hyperlink-Induced Topic Search (HITS) on the transformed data that generates an ordered list of recommended points-of-interest. Extensive experiments on a real trajectory dataset show that our algorithm is efficient, scalable and demonstrates high recommendation accuracy while meeting the required differential privacy guarantees.

Takbiri, N., Houmansadr, A., Goeckel, D. L., Pishro-Nik, H..  2017.  Limits of location privacy under anonymization and obfuscation. 2017 IEEE International Symposium on Information Theory (ISIT). :764–768.

The prevalence of mobile devices and location-based services (LBS) has generated great concerns regarding the LBS users' privacy, which can be compromised by statistical analysis of their movement patterns. A number of algorithms have been proposed to protect the privacy of users in such systems, but the fundamental underpinnings of such remain unexplored. Recently, the concept of perfect location privacy was introduced and its achievability was studied for anonymization-based LBS systems, where user identifiers are permuted at regular intervals to prevent identification based on statistical analysis of long time sequences. In this paper, we significantly extend that investigation by incorporating the other major tool commonly employed to obtain location privacy: obfuscation, where user locations are purposely obscured to protect their privacy. Since anonymization and obfuscation reduce user utility in LBS systems, we investigate how location privacy varies with the degree to which each of these two methods is employed. We provide: (1) achievability results for the case where the location of each user is governed by an i.i.d. process; (2) converse results for the i.i.d. case as well as the more general Markov Chain model. We show that, as the number of users in the network grows, the obfuscation-anonymization plane can be divided into two regions: in the first region, all users have perfect location privacy; and, in the second region, no user has location privacy.