Data Privacy and Security Considerations for Personal Assistants for Learning (PAL)

Raybourn, Elaine M.; Fabian, Nathan D.; Davis, Warren L.; Parks, Raymond C.; McClain, Jonathan T.; Trumbo, Derek; Regan, Damon; Durlach, Paula J.

doi:10.1145/2732158.2732195

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…Persuasive strategies such as monitoring, tracking, and personalization affect the privacy of users. Not only is health data sensitive, it may also trigger placebo effect [39] and/or undesired outcomes which may lead to coercion [40] and falsification of information (by the user) [41]. These issues have the tendency to affect the efficiency of an eHealth application particularly for systems that offer personalization and recommendations.…”

Section: Discussionmentioning

confidence: 99%

GDPR and Systems for Health Behavior Change: A Systematic Review

Agyei

Oinas-Kukkonen

2020

Lecture Notes in Computer Science

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eHealth systems for behavior change need to cope with a wide variety of privacy requirements specified by governmental and other regulations. We conducted a systematic review of scientific articles. Analysis of the articles revealed General Data Protection Regulation (GDPR) compliant eHealth technologies, challenges posed by GDPR as well as early solutions for them. In addition, we highlight key GDPR issues to be considered when designing persuasive technologies.

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Section: Discussionmentioning

confidence: 99%

GDPR and Systems for Health Behavior Change: A Systematic Review

Agyei

Oinas-Kukkonen

2020

Lecture Notes in Computer Science

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(Work in Progress) User-Tailored Privacy by Design

Wilkinson¹,

Sivakumar²,

Cherry³

et al. 2017

Proceedings 2017 Workshop on Usable Security

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The "privacy by design" philosophy addresses privacy aspects early in the design and development of an information system. While privacy by design solutions often provide considerable advantages over "post hoc" privacy solutions, they are usually not customized to the needs of individual users. Further, research shows that users differ substantially in their privacy management strategies. Thus, how can we support such broad privacy needs in a comprehensive and user-centered way? This paper presents the idea of user-tailored privacy by design, a design methodology that combines multiple privacy features into a single intelligent user interface. We discuss how this methodology moves beyond the "one-size-fits-all" approach of existing privacy by design solutions and the narrow focus on information disclosure of existing user-tailored privacy solutions. We illustrate our approach through an implementation of usertailored privacy by design within Facebook based on six privacy management profiles that were discovered in recent work, and subsequently extend this idea to the context of the Total Learning Architecture (TLA), which is a next generation learning platform that uses pervasive user monitoring to provide highly adaptive learning recommendations.

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Data Privacy and Security Considerations for Personal Assistants for Learning (PAL)

Cited by 2 publications

References 6 publications

GDPR and Systems for Health Behavior Change: A Systematic Review

GDPR and Systems for Health Behavior Change: A Systematic Review

(Work in Progress) User-Tailored Privacy by Design

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