AnonySense: A system for anonymous opportunistic sensing

Shin, Minho; Cornelius, Cory; Peebles, Dan; Kapadia, Apu; Kotz, David; Triandopoulos, Nikos

doi:10.1016/j.pmcj.2010.04.001

Cited by 151 publications

(132 citation statements)

References 18 publications

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“…In order to make it difficult for an adversary to link data reports to the reporter, the reports must be anonymized. However, anonymization per se is not a strong measure against privacy attacks as long as the attacker can link several data reports from the same user or can analyze the reports to infer information about the user who has performed the sensing (Shin et al 2011).…”

Section: The Challenge Of Privacymentioning

confidence: 99%

“…In AnonySense Shin et al 2011), privacy-preserving tasking is achieved as follows: a sensing task is created by an application and sent to the 'Registration Authority' (RA). RA checks the validity of the task for privacy safeness and forwards it to the Task Service (TS).…”

Section: Privacy-preserving Taskingmentioning

confidence: 99%

“…AnonySense prevents identification of the origin of the reports and the identity of the reporter by providing a mix network (MIX) for the users to send their reports to the Report Server (RS) Shin et al 2011). MIX acts as an anonymizing channel by routing reports through multiple servers, mixing similar reports from different sources and to different destinations, and inserting delays.…”

Section: Anonymous Reportingmentioning

confidence: 99%

“…In AnonySense Shin et al 2011) the geographical area is divided into large enough tiles to provide k-anonymity for the users. Instead of reporting their location, users report the tile in which they are located.…”

Section: Location Blurringmentioning

confidence: 99%

See 3 more Smart Citations

Privacy, Trust and Incentives in Participatory Sensing

Riahi

Rahman

Aberer

2016

Understanding Complex Systems

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Section: The Challenge Of Privacymentioning

confidence: 99%

Section: Privacy-preserving Taskingmentioning

confidence: 99%

Section: Anonymous Reportingmentioning

confidence: 99%

Section: Location Blurringmentioning

confidence: 99%

See 2 more Smart Citations

Privacy, Trust and Incentives in Participatory Sensing

Riahi

Rahman

Aberer

2016

Understanding Complex Systems

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“…The former, called pull-based approach, used in [9] is a proactive deployment strategy where mobile phones, with or without an implication of the participant, download directly a sensing task from a remote server. The latter, push-based approach, automatically propagates the sensing tasks to the mobiles devices of participants.…”

Section: State Of the Art And Limitationsmentioning

confidence: 99%

A preliminary investigation of user incentives to leverage crowdsensing activities

Haderer

Rouvoy

Seinturier

2013

2013 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops)

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Abstract-With the continuous emergence of smart portable devices, like TabletPC or smartphones, crowdsensing is becoming an active research topic. While exploiting the wisdom of the crowd provides huge benefits to pervasive applications, one of the key challenges remains to motivate people to contribute to flow of data by sharing some of the capabilities of their device. In this paper, we therefore report on the APISENSE platform, a participative platform that help scientists to collect realistic datasets from a population of voluntary participants. More specifically, we provide a preliminary investigation of how user incentives can be integrated in such a platform to encourage people to contribute while leaving enough flexibility to the scientist for choosing the model that fits their requirements.

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A formal adversarial perspective: Secure and efficient electronic health records collection scheme for multi‐records datasets

Kanwal

Anjum

Khan

et al. 2020

Trans Emerging Tel Tech

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The collection of private health data without compromising privacy is an imperative aspect of privacy‐aware data collection mechanisms. Privacy‐preserved data collection is achieved by anonymizing private data before its transmission from data holders to data collectors. Though there exist ample literature on private data collection for 1:1 (single record of a data holder) datasets, collecting multi‐records (multiple records of a data holder) datasets (referred to as 1:M datasets) has not received due attention from the research community. Therefore, the current studies experience serious privacy breaches in 1:M dataset thereby limiting their application in secure healthcare applications and systems. In this work, we have formally classified main privacy disclosures on these data collection mechanisms and proposed an improved privacy scheme, namely, horizontal sliced permuted permutation (H‐SPP) for 1:M datasets. It uses the composite slicing and anatomy‐based approach to protect against the privacy violations like identity, attribute, and membership disclosures. Moreover, we perform formal modeling of the proposed scheme using high‐level Petri nets (HLPN) and show that it effectively prevents the identified external and internal privacy attacks. Experimental results show that H‐SPP provides robust privacy for health data with high performance.

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AnonySense: A system for anonymous opportunistic sensing

Cited by 151 publications

References 18 publications

Privacy, Trust and Incentives in Participatory Sensing

Privacy, Trust and Incentives in Participatory Sensing

A preliminary investigation of user incentives to leverage crowdsensing activities

A formal adversarial perspective: Secure and efficient electronic health records collection scheme for multi‐records datasets

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