Maintaining Privacy in Pervasive Computing — Enabling Acceptance of Sensor-Based Services

Soppera, Andrea; Burbridge, Trevor

doi:10.1023/b:bttj.0000047125.97546.4a

Cited by 30 publications

(15 citation statements)

References 21 publications

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“…Further, the initial cost of the sensor is small, its physical footprint is negligible and it can be easily secured. We do not expect significant 'sensor vandalism' [5] given the low cost of the sensors, the security of the university campus and the nonintrusive nature of the sensing [6]. However a planned future project will look at new ways to assure the trustworthiness of this infrastructure.…”

Section: B Benefitsmentioning

confidence: 99%

Supersensors: Raspberry Pi Devices for Smart Campus Infrastructure

Hentschel

Jacob

Singer

et al. 2016

2016 IEEE 4th International Conference on Future Internet of Things and Cloud (FiCloud)

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Abstract-We describe an approach for developing a campuswide sensor network using commodity single board computers. We sketch various use cases for environmental sensor data, for different university stakeholders. Our key premise is that supersensors-sensors with significant compute capabilityenable more flexible data collection, processing and reaction. In this paper, we describe the initial prototype deployment of our supersensor system in a single department at the University of Glasgow.

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Section: B Benefitsmentioning

confidence: 99%

Supersensors: Raspberry Pi Devices for Smart Campus Infrastructure

Hentschel

Jacob

Singer

et al. 2016

2016 IEEE 4th International Conference on Future Internet of Things and Cloud (FiCloud)

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“…many 'concerned' individuals share great amounts of personal information (Acquisti and Gross, 2006). Soppera and Burbridge (2006) raise a range of privacy concerns in relation to the emergence of pervasive computing technologies that are embedded in the fabric of everyday life. They first note that these devices will "disappear so effectively that end users will lose awareness of the devices' presence or purpose", and in this case, they point out that if "you cannot interact with the computer, how can you tell what data is collected, where the data is flowing to, and more importantly, what the consequences of your actions are?".…”

Section: Privacymentioning

confidence: 99%

“…In many cases shared technologies will capture personal and potentially sensitive data about individuals. For example monitoring appliances in the home can lead to personally--identifiable information being collected about the activities of members of a household (Soppera and Burbridge, 2006 ;Karotz, 2012). Research is required to understand how people interpret and react to these new kinds of data in order to inform design and identify the social consequences of introducing these technologies.…”

Section: Introductionmentioning

confidence: 99%

Exploring Interpretations of Data from the Internet of Things in the Home

Brown

Coughlan

Lawson

et al. 2013

Interacting with Computers

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The 'Internet of Things' can be expected to radically increase the amount of potentially sensitive data gathered in our homes. This study explores the social implications of the presentation of data that could be collected within the household. In particular it focuses on how ambiguities in this data, combined with existing interpersonal relationships, could influence social dynamics. Thirty--five participants were each presented with three separate household scenarios, involving ambiguous data which were collected and presented via near--future Internet of Things technologies. Each participant was asked to respond to a series of open and closed questions about how they would interpret the data, how they would react to it, and their general opinions of the technologies presented. Through qualitative and quantitative analysis of their responses, we contribute an understanding of how people interpret information about those around them. We find a common willingness to make inferences based on ambiguities within the data, even when participants are aware of the limitations of their understanding. We also find that sharing data produced via tagging of everyday objects raises a high level of privacy concern, and that in a somewhat incoherent stance users are more comfortable sharing data publicly than in a targeted fashion with commercial organisations. Our findings also suggest that the age of the target user group has a greater impact on ease of use judgements than the nature of the technology, and we find some evidence that user's interpretations can be biased by an individual's age.

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“…Because of the policy complexity, these P3P-based approaches are believed not to be the solution for pervasive computing environments [13]. Leonhardt and Magee use high level policies for access control and privacy protection in pervasive computing environments [7].…”

Section: Related Workmentioning

confidence: 99%

“…Confab's data model is used to represent context information including locations, activities, and services. Confab and other frameworks [1,13] do not provide sophisticated methods to help users make decisions. Our approach complements the frameworks and improves the usability by helping users to make rational decisions.…”

Section: Related Workmentioning

confidence: 99%

Rational exposure: A game theoretic approach to optimize identity exposure in pervasive computing environments

Zhu

Zhu²

2009

2009 IEEE International Conference on Pervasive Computing and Communications

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Abstract-In pervasive computing environments, personal information is expressed in digital forms to a great extent. Daily activities and personal preferences may be easily associated with personal identities. Privacy protection is a serious challenge. The fundamental problem is the lack of a mechanism to help people expose appropriate amounts of their identity information. We propose the Hierarchical Identity model. It expresses one's identity information from precise to general. We model privacy exposure as an extensive game. By finding the subgame perfect equilibria in the games, our approach achieves optimal exposure. It finds the most general identity information that a user wants to expose and a service provider accepts.

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Maintaining Privacy in Pervasive Computing — Enabling Acceptance of Sensor-Based Services

Cited by 30 publications

References 21 publications

Supersensors: Raspberry Pi Devices for Smart Campus Infrastructure

Supersensors: Raspberry Pi Devices for Smart Campus Infrastructure

Exploring Interpretations of Data from the Internet of Things in the Home

Rational exposure: A game theoretic approach to optimize identity exposure in pervasive computing environments

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