Privacy by Design - Principles of Privacy-Aware Ubiquitous Systems

By Marc Langheinrich. Ubiquitous Computing, 2001

1,089 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/e664af0e871ea3346320f3a16be2d85db69139ee

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In Computer Vision and Pattern Recognition

In Information Systems

In Sociology and Political Science

Citations in the corpus

Freedom and Privacy in Ambient Intelligence (2005, background): “Marc Langheinrich has asked this question in relation to Ubiquitous Computing and has argued that it has four special properties (Langheinrich 2001): (1) Ubiquity.”
Privacy risk models for designing privacy-sensitive ubiquitous computing systems (2004, background): “Langheinrich looked at how the fair information practices can be adapted for ubicomp scenarios, providing many examples of how these practices might influence the design of such applications [22].”
Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces (2018, background): “The Privacy by Design [10, 25] principle sets privacy as the default standard in the design and engineering of a system.”
Requirements and design space for interactive public displays (2010, background): “Langheinrich [33] explains the guiding principles of anonymity, access, locality, adequate security, notice and disclosure, and choice.”
Intelligent Products: A survey (2009, background): “Also, challenges related to privacy, security, trust etc. are not discussed in detail here, but such aspects in the context of Intelligent Products are discussed in detail in e.g. [35] [50] [51] [71].”
The Java Context Awareness Framework (JCAF) - A Service Infrastructure and Programming Framework for Context-Aware Applications (2005, background): “However, in line with [20] we argue for supporting adequate security in a ubicomp environment.”
Personal privacy through understanding and action: five pitfalls for designers (2004, background): “In reaching for this middle ground, we have tried to honor the fair information practices, as developed by Westin [42] and more recently promoted by Langheinrich [26], and to minimize information asymmetry between users and observers, as argued by Jiang et al. [24].”
Semantic Web in the context broker architecture (2004, background): “BrA follows the principle of proximity and locality [ 14 ], exploiting the locality information of the users for enforcing access restrictions to their personal information.”
An architecture for privacy-sensitive ubiquitous computing (2004, neutral): “The end-user needs for Confab were gathered through several scenario-based interviews on location-enhanced applications we performed with twenty people of various ages and professions; extended analysis of freeform comments we performed on a previous survey on ubicomp privacy preferences [46]; analy”
Location privacy in pervasive computing (2008, background): “However, there are situations where anonymity or partial identification are not viable options for the provision of online services [33].”
A Survey on Ambient Intelligence in Health Care (2013, neutral): “To allow for provision of healthcare in such a system and, at the same time, to maintain privacy, a privacy-bydesign (PbD) approach can be considered [254].”
A Formal Model of Obfuscation and Negotiation for Location Privacy (2005, methodology): “By contrast, in the model and algorithm presented in this paper, we aim to enable an individual’s actual identity to be revealed (thereby facilitating authentication and personalization [18]) at the same time as maintaining that individual’s location privacy.”
Education for AI, not AI for Education: The Role of Education and Ethics in National AI Policy Strategies (2021, background): “Additional promising frameworks that were not developed specifically for AI include Responsible Research and Innovation (Schomberg, 2013), Privacy by Design (Oetzel and Spiekermann 2014), Ethics by Design (Dignum, 2018), Value Sensitive Design (Friedman, Hendry, and Boning 2017), and others.”
Privacy policies as decision-making tools: an evaluation of online privacy notices (2004, background): “Closer to home, there is a body of HCI literature on designing for privacy [12], mostly focusing on the problems associated with groupware and ubiquitous computing [4, 6, 10].”
Security in the Internet of Things: A Review (2012, background): “In the literature [7], several guidelines are given to solve this problem in the design phase: at first users must know that they are being sensed, the second users must be able to choose whether they are being sensed or not, the third users must be able to remain anonymous.”
Survey on UAV Cellular Communications: Practical Aspects, Standardization Advancements, Regulation, and Security Challenges (2018, background): “Privacy Act of 1974 [160], [161], these rules might be out-of-date due to the rapid development of emerging technologies.”
A Design Space for Effective Privacy Notices (2015, background): “FTC chairwoman Ramirez explains this rationale as follows [107]: “Consumers know, for instance, that a smart thermostat is gathering information about their heating habits, and that a fitness band is collecting data about their physical activity.”
Governance of algorithms: options and limitations (2015, background): “Suppliers of services that rely on algorithmic selection can commit themselves to certain “values” (Introna and Nissenbaum, 2000), such as search neutrality or the “minimum principle” of data collection (Langheinrich, 2001; Cavoukia, 2009).”