A Location Privacy Aware Friend Locator

Šikšnys, Laurynas; Thomsen, Jeppe Rishede; Šaltenis, Simonas; Yiu, Man Lung; Andersen, Ove

doi:10.1007/978-3-642-02982-0_29

Cited by 48 publications

(40 citation statements)

References 11 publications

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“…In privacy preservation, Proximity Testing can be used to privately and "continuously report all events of mobile users being within the distance of each other" [10]. This can be used for the task of finding sensors that have measured the same phenomenon at approximately the same time and location.…”

Section: Background and Related Workmentioning

confidence: 99%

Privacy-Preserving Collaborative Blind Macro-Calibration of Environmental Sensors in Participatory Sensing

Markert¹,

Budde²,

Schindler³

et al. 2018

EAI Endorsed Transactions on Internet of Things

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The ubiquity of ever-connected smartphones has lead to new sensing paradigms that promise environmental monitoring in unprecedented temporal and spatial resolution. Everyday people may use low-cost sensors to collect environmental data. However, measurement errors increase over time, especially with low-cost air quality sensors. Therefore, regular calibration is important. On a larger scale and in participatory sensing, this needs be done in-situ. Since for this step, personal sensor data, time and location need to be exchanged, privacy implications arise. This paper presents a novel privacy-preserving multi-hop sensor calibration scheme, that combines Private Proximity Testing and an anonymizing MIX network with cross-sensor calibration based on rendezvous. Our evaluation with simulated ozone measurements and real-world taxicab mobility traces shows that our scheme provides privacy protection while maintaining competitive overall data quality in dense participatory sensing networks.

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Section: Background and Related Workmentioning

confidence: 99%

Privacy-Preserving Collaborative Blind Macro-Calibration of Environmental Sensors in Participatory Sensing

Markert¹,

Budde²,

Schindler³

et al. 2018

EAI Endorsed Transactions on Internet of Things

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“…The main privacy concern in proximity test is that user's actual location may be involuntarily revealed to either the server or other users. To this end, a privacy-preserving proximity test solution is proposed in [21], using a grid-based encryption algorithm. In [14], Mascetti et.…”

Section: Related Workmentioning

confidence: 99%

“…Efficiency: Existing private proximity test protocols [17,21] operate on pairs of users. If Alice wants to test a group of n users, she has to run the protocol n times with each and every user in the group.…”

Section: Design Challenges and Goalsmentioning

confidence: 99%

“…However, the initial trust establishment among users in a large-scale mobile social network remains a difficult task, simply because managing shared keys with everyone else is not scalable without a trusted authority. Most existing solutions to date have relied on a-priori shared secrets between each pair of users [17,21], which severely limits their applicability and scalability. Finally, efficiency and usability need to be achieved simultaneously.…”

Section: Introductionmentioning

confidence: 99%

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SHARP: Private Proximity Test and Secure Handshake with Cheat-Proof Location Tags

Zheng

Lou

et al. 2012

Computer Security – ESORICS 2012

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Abstract.A location proximity test service allows mobile users to determine whether they are in close proximity to each other, and has found numerous applications in mobile social networks. Unfortunately, existing solutions usually reveal much of users' private location information during proximity test. They are also vulnerable to location cheating where an attacker reports false locations to gain advantage. Moreover, the initial trust establishment among unfamiliar users in large scale mobile social networks has been a challenging task. In this paper, we propose a novel scheme that enables a user to perform (1) privacy-preserving proximity test without revealing her actual location to the server or other users not within the proximity, and (2) secure handshake that establishes secure communications among stranger users within the proximity who do not have pre-shared secret. The proposed scheme is based on a novel concept, i.e. location tags, and we put forward a location tag construction method using environmental signals that provides location unforgeability. Bloom filters are used to represent the location tags efficiently and a fuzzy extractor is exploited to extract shared secrets between matching location tags. Our solution also allows users to tune their desired location privacy level and range of proximity. We conduct extensive analysis and real experiments to demonstrate the feasibility, security, and efficiency of our scheme.

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“…Reliable and secure location-based services demand secure and accurate proximity tests, which allow radio users and/or service providers to determine whether a client is located within the same geographic region [1]- [4]. In order to support the business or financial oriented LBS services, proximity tests have to provide location privacy protection and location unforgeability [5]- [9].…”

Section: Introductionmentioning

confidence: 99%

Proximity-Based Security Techniques for Mobile Users in Wireless Networks

Xiao

Yan

Lou

et al. 2013

IEEE Trans.Inform.Forensic Secur.

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Abstract-In this paper, we propose a privacy-preserving proximity-based security system for location-based services (LBS) in wireless networks, without requiring any pre-shared secret, trusted authority or public key infrastructure. In this system, the proximity-based authentication and session key establishment are implemented based on spatial temporal location tags. Incorporating the unique physical features of the signals sent from multiple ambient radio sources, the location tags cannot be easily forged by attackers. More specifically, each radio client builds a public location tag according to the received signal strength indicators (RSSI), sequence numbers and MAC addresses of the ambient packets. Each client also keeps a secret location tag that consists of the packet arrival time information to generate the session keys. As clients never disclose their secret location tags, this system is robust against eavesdroppers and spoofers outside the proximity range. The system improves the authentication accuracy by introducing a nonparametric Bayesian method called infinite Gaussian mixture model in the proximity test and provides flexible proximity range control by taking into account multiple physical-layer features of various ambient radio sources. Moreover, the session key establishment strategy significantly increases the key generation rate by exploiting the packet arrival time of the ambient signals. The authentication accuracy and key generation rate are evaluated via experiments using laptops in typical indoor environments.

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A Location Privacy Aware Friend Locator

Cited by 48 publications

References 11 publications

Privacy-Preserving Collaborative Blind Macro-Calibration of Environmental Sensors in Participatory Sensing

Privacy-Preserving Collaborative Blind Macro-Calibration of Environmental Sensors in Participatory Sensing

SHARP: Private Proximity Test and Secure Handshake with Cheat-Proof Location Tags

Proximity-Based Security Techniques for Mobile Users in Wireless Networks

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