Location privacy via actively secure private proximity testing

Nielsen, Janus Dam; Pagter, Jakob; Stausholm, Michael

doi:10.1109/percomw.2012.6197514

Cited by 28 publications

(28 citation statements)

References 12 publications

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“…The solutions proposed for producing a discrete version of continuous domains have the goal of making data suitable to machine learning and/or data mining applications, of supporting proximity tests, or of anonymizing pseudonyms (e.g., [6,9,11,14]). These solutions are therefore not suited to the scenario considered in this paper.…”

Section: Related Workmentioning

confidence: 99%

Integrity for Approximate Joins on Untrusted Computational Servers

Vimercati

Foresti

Jajodia

et al. 2015

ICT Systems Security and Privacy Protection

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Abstract. In the last few years, many efforts have been devoted to the development of solutions aiming at ensuring the confidentiality and integrity of data and computations in the cloud. In particular, a recent solution for verifying the integrity of equi-join queries is based on the insertion of checks (markers and twins) whose presence provides probabilistic guarantees on the integrity of the computation. In this paper, we propose an approach for verifying the integrity of approximate join queries, which is based on the introduction of a discretized version of the join attribute and on the translation of the approximate join into an equijoin defined over the discrete attribute added to the original relations. The proposed approach guarantees the correctness and completeness of the join result, while causing a limited overhead for the user.

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Section: Related Workmentioning

confidence: 99%

Integrity for Approximate Joins on Untrusted Computational Servers

Vimercati

Foresti

Jajodia

et al. 2015

ICT Systems Security and Privacy Protection

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“…As mentioned in Section I, in this paper, we focus on the data collection of environmental signals as ambient information of users to construct the list of the ambient information, which is utilized for more secure authentication systems [5], [6]. In [5], [6], as the ambient information, some elements such as Service Set IDentifier (SSID), Received Signal Strength (RSS), Sequence Numbers (SN) and Media Access Control (MAC) addresses of the packets are introduced.…”

Section: A Assumed Network Modelmentioning

confidence: 99%

“…In [5], [6], as the ambient information, some elements such as Service Set IDentifier (SSID), Received Signal Strength (RSS), Sequence Numbers (SN) and Media Access Control (MAC) addresses of the packets are introduced. As shown in Fig.…”

Section: A Assumed Network Modelmentioning

confidence: 99%

A method for collecting uniform amount of fresh data from areas with varying population density

Kawamoto

Nakazawa

Nishiyama

et al. 2015

2015 IEEE International Conference on Communications (ICC)

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With the development of wireless communication technology, the utilization of the ambient information that the network users observe has attracted much attention. In this paper, we focus especially on the utilization of observed environmental signals for authentication systems. Ambient information taken as unique data at a particular time and place can be utilized to construct stronger authentication systems. However, since the environmental condition of the network is different for each location, which has a huge effect on the observed ambient information in the area, required data are also different for each place. Thus, in this paper, we propose an efficient data collection method which dynamically changes the way data is collected according to the requirements and the network condition. More specifically, our proposal aims to collect uniform amount of fresh data from areas with varying population density. Additionally, an algorithm to improve the efficiency of our proposal regarding the accommodation of the environmental condition of the network is introduced. Moreover, numerical results verify the effectiveness of our proposal.

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“…Location tags and proximity tests are also used in [19], as a way of providing local authentication, while [47] presents a secure handshake for communication between the two actors in proximity. The security of the basic proximity testing protocol has been further improved in [31]. In [43], Tonicelli et al propose a solution for proximity testing based on pre-distributed data, secure in the Universal Composability framework.…”

Section: Introductionmentioning

confidence: 99%

Location privacy without mutual trust: The spatial Bloom filter

Calderoni

Palmieri

Maio

2015

Computer Communications

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Access to the full text of the published version may require a subscription. Rights AbstractLocation-aware applications are one of the biggest innovations brought by the smartphone era, and are effectively changing our everyday lives. But we are only starting to grasp the privacy risks associated with constant tracking of our whereabouts. In order to continue using location-based services in the future without compromising our privacy and security, we need new, privacy-friendly applications and protocols. In this paper, we propose a new compact data structure based on Bloom filters, designed to store location information. The Spatial Bloom Filter (SBF), as we call it, is designed with privacy in mind, and we prove it by presenting two private positioning protocols based on the new primitive. The protocols keep the user's exact position private, but allow the provider of the service to learn when the user is close to specific points of interest, or inside predefined areas. At the same time, the points and areas of interest remain oblivious to the user. The two proposed protocols are aimed at different scenarios: a two-party setting, in which communication happens directly between the user and the service provider, and a three-party setting, in which the service provider outsources to a third party the communication with the user. A detailed evaluation of the efficiency and security of our solution shows that privacy can be achieved with minimal computational and communication overhead. The potential of spatial Bloom filters in terms of generality, security and compactness makes them ready for deployment, and may open the way for privacy preserving location-aware applications.

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Location privacy via actively secure private proximity testing

Cited by 28 publications

References 12 publications

Integrity for Approximate Joins on Untrusted Computational Servers

Integrity for Approximate Joins on Untrusted Computational Servers

A method for collecting uniform amount of fresh data from areas with varying population density

Location privacy without mutual trust: The spatial Bloom filter

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