Regulatory Model for AAL

Pedraza, Juanita; Patricio, Miguel Á.; Asís, Agustín de

doi:10.1007/978-3-642-19644-7_20

Cited by 3 publications

(2 citation statements)

References 24 publications

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“…For a full description of the AAL domain in which the identification system has been used, see [37][38][39] describ-ing the AAL system and giving some illustrative examples. Our aim was to build an identification system into these applications, and we conducted a legal analysis of requirements [40].The legal analysis governed by privacy-by-design rules led to the development of an identification system capable of identifying user membership of a pre-defined class but avoiding personal identification.…”

Section: Privacy By Design In a Face Recognition Systemmentioning

confidence: 99%

Privacy-by-design rules in face recognition system

2013

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Abstract:In this paper, we develop a face recognition system based on softcomputing techniques, which complies with privacy-by-design rules and defines a set of principles that are context-aware applications (including biometric sensors) and should contain to conform to European and US law. This paper deals with the necessity to consider legal issues concerning privacy or human rights in the development of biometric identification in ambient intelligence systems. Clearly, context-based services and ambient intelligence (and the most promising research area in Europe, namely ambient assisted living, ALL) call for a major research effort on new identification procedures.

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Section: Privacy By Design In a Face Recognition Systemmentioning

confidence: 99%

Privacy-by-design rules in face recognition system

2013

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“…Others propose technology-specific principles: Abdul-Ghani and Konstantas (2019), Perera et al (2016), Perera et al (2020) and the UK Government (Department for Digital Culture Media & Sport, 2018) 'Secure by Design' for IoT devices, Sedenberg et al (2016) for robots, Pedraza et al (2011Pedraza et al ( , 2013 for facial recognition systems, Pinkas (2016) for eID systems, and Vanezi et al ( 2019) for e-learning platforms. We excluded all of these from further analysis because we are looking for generally applicable privacy attributes.…”

Section: Privacy By Design Principlesmentioning

confidence: 99%

Data, data and even more data : empowering users to make well-informed decisions about online privacy

Barth¹

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particular context and focusing on actual use of data and intended outcomes of data gathering might prove successful. To date, the balance between granularity and generalization of contexts remains unresolved (Solove, 2002). However, regulations specifying concrete conditions for processing personal data "are the decisive test for discerning whether [or not] society is aware of this price and willing to pay it. If the signs of experience are correct, this payment can be delayed no further" (Simitis, 1987; p. 746). Beyond access control and authentication:A technical perspective on information privacy "Computers constantly produce data. It's their input and output, but it's also a by-product of everything they do…they sense and record more than you're aware of" (Schneier, 2015, p. 15).The era of big data and unprecedented interconnectivity has allowed companies to amass datasets containing incredible amounts of personal information. By recording, storing, tracking and linking events over time, computers are able to identify patterns and gain insights into the individual preferences, interests or personal characteristics of users. As a result, behavioral tracking-and the profiling it makes possible-stand to become one of the most serious threats to privacy ever known.Techniques such as browser cookies and tracking 'pixels' allow monitoring the behavior of individual users across the internet. Cookies were designed to allow websites to recognize visitors to provide features such as automatic login, shopping carts, language settings, or saving preferences. However, advertising companies are increasingly using this technology on a large scale to record the activity of users across a large number of websites (Castelluccia, 2012;Nikiforakis et al., 2013). They do so by asking website owners to insert a small script (also known as a 'tracking pixel') into their pages in exchange for usage statistics. This allows advertising companies to create surprisingly accurate profiles of individuals based on their online behavior, potentially including information they might not even be aware of themselves (Gutwirth & Hildebrandt, 2008). These profiles are usually used to target ads, but they are also sold and shared, enabling discrimination and manipulation (Lyon, 2003;Zarsky, 2019). This kind of tracking is often invisible to users. Even if cookies are disabled or blocked, 'fingerprinting' allows tracking scripts to identify individuals based on specific configurations that are unique to the browser. This allows tracking users between 24 | Chapter 1 2 The privacy paradox -Investigating discrepancies between expressed privacy concerns and actual online behavior -A systematic literature review Barth, S., & De Jong, M. D. T. (2017). The privacy paradox -Investigating discrepancies between expressed privacy concerns and actual online behavior -A systematic literature review.

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Understanding Online Privacy—A Systematic Review of Privacy Visualizations and Privacy by Design Guidelines

Barth

Hartel

2022

ACM Comput. Surv.

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Privacy visualizations help users understand the privacy implications of using an online service. Privacy by Design guidelines provide generally accepted privacy standards for developers of online services. To obtain a comprehensive understanding of online privacy, we review established approaches, distill a unified list of 15 privacy attributes and rank them based on perceived importance by users and privacy experts. We then discuss similarities, explain notable differences, and examine trends in terms of the attributes covered. Finally, we show how our results provide a foundation for user-centric privacy visualizations, inspire best practices for developers, and give structure to privacy policies.

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Regulatory Model for AAL

Cited by 3 publications

References 24 publications

Privacy-by-design rules in face recognition system

Privacy-by-design rules in face recognition system

Data, data and even more data : empowering users to make well-informed decisions about online privacy

Understanding Online Privacy—A Systematic Review of Privacy Visualizations and Privacy by Design Guidelines

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