Myths and fallacies of "Personally Identifiable Information"

By Arvind Narayanan, Vitaly Shmatikov. 2010

380 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Sociology and Political Science

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Citations in the corpus

Privacy in Pharmacogenetics: An End-to-End Case Study of Personalized Warfarin Dosing (2014, positive): “Prior works [36] [37] in non-medical settings have shown that leaking datasets can enable de-anonymization of users and other privacy risks.”
Faceless Person Recognition: Privacy Implications in Social Media (2016, background): “Privacy of users in social network is a well studied topic in the security community [2, 3, 1, 4].”
Ethical and privacy principles for learning analytics (2014, background): “It is with the inclusion of such ample terms that the definition becomes much broader (Narayanan & Shmatikov, 2010).”
Data Matching (2012, background): “Narayanan and Shmatikov recently provided some general insight into what personally identifying information is, and myth and fallacies surrounding such information [194].”
Twenty Security Considerations for Cloud-Supported Internet of Things (2016, background): “However, even if individual data streams are themselves benign, the application of data in combination can raise serious privacy and security concerns [42].”
Privacy in the Internet of Things: threats and challenges (2014, background): “Those approaches are difﬁcult to ﬁt to the IoT: Most data anonymization techniques can be broken using auxiliary data [78, 79, 80], that is likely to become available at some point during the IoT evolution.”
Scalable and Private Media Consumption with Popcorn (2016, background): “Philosophically, privacy advocates observe that freedom requires the ability to consume privately [88]; practically, an entity with access to a person's consumption profile can infer the person's sexual orientation, political leanings, private cultural affiliations , etc. [74] [75] [90].”
Securing Fog Computing for Internet of Things Applications: Challenges and Solutions (2018, background): “However, even the collected data seems to be non-sensitive, the application of data in combination can trigger serious security and privacy concerns [215].”
An Intelligence in Our Image: The Risks of Bias and Errors in Artificial Intelligence (2017, background): “Researchers are reporting that traditional expectations of data privacy and anonymity are no longer attainable (Dwork, 2008a; Narayanan and Shmatikov, 2010; Ohm, 2010).”
MoodScope: building a mood sensor from smartphone usage patterns (2013, neutral): “However, unintended leakage of information when handling rich high-dimensional personal data can easily occur [37].”
Is privacy privacy? (2018, background): “In fact, to be effective, de-identification must strip the data of most of its informational content, rendering it almost void of value of analytic purposes (see discussion in [22,23]).”
Networked privacy: How teenagers negotiate context in social media (2014, background): “Sophisticated “re-identification” algorithms make this even simpler; as Narayanan and Shmatikov (2010) write, “The versatility and power of re-identification algorithms imply that terms such as ‘personally identifiable’ and ‘quasi-identifier’ simply have no technical meaning” (p. 26).”
Engineering Privacy by Design (2011, background): “, impossibility of anonymization [18, 38, 43], and require us to think ways of applying these results in systems.”
Differentially Private Matrix Completion, Revisited (2017, background): “Naturally, personalization problems require collecting and analyzing sensitive customer data like their preferences for various items, which can lead to serious privacy breaches [7, 33, 37].”
Adversarial Image Perturbation for Privacy Protection A Game Theory Perspective (2017, background): “While there exists a bulk of research on user privacy traditionally led by the security community [22, 39, 23, 19], studies on private content in visual data began only recently [37, 28, 18].”
Differential Privacy Techniques for Cyber Physical Systems: A Survey (2018, background): “However, these cleaned and protected datasets can easily expose certain PII when they are analysed and mixed with different other feature sets [239].”