Implicit Contextual Integrity in Online Social Networks

Criado, Natalia; Such, José M.

doi:10.1016/j.ins.2015.07.013

Cited by 35 publications

(23 citation statements)

References 52 publications

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“…CI has been used in many legal and computer science studies as an alternative framework to define and reason about privacy [24,51,52]. The computer science community has focused on formal expression of contextual privacy norms to detect infraction using formal logic and to propose methods for accountability and enforcement [6,9,12]. Legal privacy scholars have used CI as a new lens to re-evaluate existing regulations, such as the meaning of "reasonable expectation of privacy" in the Fourth Amendment [49], and to find gaps in the privacy settings of existing systems, including Facebook [29] and Google [62].…”

Section: Contextual Integrity Backgroundmentioning

confidence: 99%

Discovering Smart Home Internet of Things Privacy Norms Using Contextual Integrity

Apthorpe

Shvartzshnaider

Mathur

et al. 2018

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

111

123

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The proliferation of Internet of Things (IoT) devices for consumer "smart" homes raises concerns about user privacy. We present a survey method based on the Contextual Integrity (CI) privacy framework that can quickly and efficiently discover privacy norms at scale. We apply the method to discover privacy norms in the smart home context, surveying 1,731 American adults on Amazon Mechanical Turk. For $2,800 and in less than six hours, we measured the acceptability of 3,840 information flows representing a combinatorial space of smart home devices sending consumer information to first and third-party recipients under various conditions. Our results provide actionable recommendations for IoT device manufacturers, including design best practices and instructions for adopting our method for further research.CCS Concepts: • Security and privacy → Human and societal aspects of security and privacy; Privacy protections;• Human-centered computing → Empirical studies in ubiquitous and mobile computing;In this paper, we present a general, scalable survey method for discovering consumer privacy norms based on the Contextual Integrity (CI) privacy framework [44] (Section 3). CI is a well-established theory that defines privacy norms as the generally accepted appropriateness of specific information exchanges, or "information flows," in specific contexts. Information flows and associated contexts can be described using five parameters: sender, recipient, subject, attribute, and transmission principle. This precise formulation makes it possible to thoroughly investigate the combinatorial space of contextual information flows and associated privacy norms with an automated, large-scale survey on a crowdsourcing platform. Our use of CI also ensures that the method is repeatable, both for the same types of devices over time, as well as for entirely new classes of devices.The method we develop is effective for discovering privacy norms in general. In this paper, we focus on applying the method to discover smart home privacy norms. We conducted a survey with a population of 1,731 adults from the United States on the Amazon Mechanical Turk (MTurk) platform. The survey cost $2,800 and allowed us to query the acceptability of 3,840 information flows involving smart home devices in less than six hours and identify associated privacy norms (Section 4). Our results provide insightful observations and actionable recommendations for IoT device manufacturers, regulators, and consumer advocates (Section 5).Device manufacturers can use our survey method to perform their own research on how consumers might view the use of data that their products collect. We designed the method to make it easy to customize with new information flows and contexts, allowing manufacturers to discover privacy norms relevant to specific products, including ones we have not studied in this paper. The results will indicate whether existing or proposed devices may violate established privacy norms, providing an opportunity to preempt negative user feedback, public relati...

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Section: Contextual Integrity Backgroundmentioning

confidence: 99%

Discovering Smart Home Internet of Things Privacy Norms Using Contextual Integrity

Apthorpe

Shvartzshnaider

Mathur

et al. 2018

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

111

123

View full text Add to dashboard Cite

show abstract

“…Our system, being logic based, has the capability to provide informative recommendations to the users, while guiding them in their sharing decisions and as well as in the modification and creation of their defined OSN groups. This is also an advantage of our approach over Criado and Such's computational model of contextual integrity [9]. The other techniques proposed by Mugan et al and Crenshaw et al employ machine learning techniques to enhance adjustment of complex privacy preferences.…”

Section: Related Workmentioning

confidence: 91%

“…Their work is based on Helen Nissenbaum's "contextual theory", which is a privacy theory conceptualising the appropriateness of information sharing based on the contexts in which the information is shared [31]. This work has inspired the development of quantitative computational model of "contextual integrity", where agents can infer users' privacy norms from their sharing behaviour [9]. The authors model the "context" in the form of a group of users, which have similarities with our definition of social identity groups.…”

Section: Related Workmentioning

confidence: 99%

Privacy dynamics

Çalıklı

Law

Bandara

et al. 2016

Proceedings of the 11th International Symposium on Software Engineering for Adaptive and Self-Managing Systems

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Privacy violations in online social networks (OSNs) often arise as a result of users sharing information with unintended audiences. One reason for this is that, although OSN capabilities for creating and managing social groups can make it easier to be selective about recipients of a given post, they do not provide enough guidance to the users to make informed sharing decisions. In this paper we present Privacy Dynamics, an adaptive architecture that learns privacy norms for different audience groups based on users' sharing behaviours. Our architecture is underpinned by a formal model inspired by social identity theory, a social psychology framework for analysing group processes and intergroup relations. Our formal model comprises two main concepts, the group membership as a Social Identity (SI) map and privacy norms as a set of conflict rules. In our approach a privacy norm is specified in terms of the information objects that should be prevented from flowing between two conflicting social identity groups. We implement our formal model by using inductive logic programming (ILP), which automatically learns privacy norms. We evaluate the performance of our learning approach using synthesised data representing the sharing behaviour of social network users.

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“…Multiple copresent audiences make upholding contextual information flow norms difficult (see Acquisti & Gross, 2006;boyd, 2010;Marder et al, 2012). The contextual integrity theory of privacy has seen extensive influenceincluding formalised models of contextual integrity that map norms of transmission between networks (Barth, Datta, Mitchell, & Nissenbaum, 2006;Criado & Such, 2015) and the elicitation and analysis of privacy requirements for computer software (Thomas, Bandara, Price, & Nuseibeh, 2014).…”

Section: Privacy Is Contextually Dependentmentioning

confidence: 99%

The psychology of privacy in the digital age

Stuart

Bandara

Levine

2019

Social & Personality Psych

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Privacy is a psychological topic suffering from historical neglect-a neglect that is increasingly consequential in an era of social media connectedness, mass surveillance, and the permanence of our electronic footprint. Despite fundamental changes in the privacy landscape, social and personality psychology journals remain largely unrepresented in debates on the future of privacy. By contrast, in disciplines like computer science and media and communication studies, engaging directly with sociotechnical developments, interest in privacy has grown considerably.In our review of this interdisciplinary literature, we suggest four domains of interest to psychologists. These are as follows: sensitivity to individual differences in privacy disposition, a claim that privacy is fundamentally based in social interactions, a claim that privacy is inherently contextual, and a suggestion that privacy is as much about psychological groups as it is about individuals. Moreover, we propose a framework to enable progression to more integrative models of the psychology of privacy in the digital age and in particular suggest that a group and social relations-based approach to privacy is needed.Recent developments in political, technological, and social domains are leading to a direct challenge to our ability to exercise privacy. The privacy and security expert Bruce Schneier claims that surveillance is now the business model of the Internet (2015). Rather than being the customers of large digital technology companies, we are the product (Rushkoff, 2011;Schneier, 2015). Facebook founder Mark Zuckerberg (2010) has famously argued that the need for privacy is over and that withholding information could be seen as a selfish act. In the political sphere, privacy has moved centre stage after the revelations from Edward Snowden about the dragnet surveillance carried out by the

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Implicit Contextual Integrity in Online Social Networks

Cited by 35 publications

References 52 publications

Discovering Smart Home Internet of Things Privacy Norms Using Contextual Integrity

Discovering Smart Home Internet of Things Privacy Norms Using Contextual Integrity

Privacy dynamics

The psychology of privacy in the digital age

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