Predicting Personality Using Novel Mobile Phone-Based Metrics | de Montjoye, Quoidbach, Robic, Pentland

Yves-Alexandre de Montjoye, Jordi Quoidbach, Florent Robic, Alex (Sandy) Pentland; Predicting Personality Using Novel Mobile Phone-Based Metrics; In: A.M. Greenberg, W.G. Kennedy, N.D. Bos (editors) Social Computing, Behavioral-Cultural Modeling and Prediction as Proceedings of Social Computing, Behavioral (SBP 2013), Lecture Notes in Computer Science, vol 7812; 2013; paywalls: Springer, ACM.

Abstract

The present study provides the first evidence that personality can be reliably predicted from standard mobile phone logs. Using a set of novel psychology-informed indicators that can be computed from data available to all carriers, we were able to predict users’ personality with a mean accuracy across traits of 42% better than random, reaching up to 61% accuracy on a three-class problem. Given the fast growing number of mobile phone subscription and availability of phone logs to researchers, our new personality indicators open the door to exciting avenues for future research in social sciences. They potentially enable cost-effective, questionnaire-free investigation of personality-related questions at a scale never seen before.

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On Virtual Economies | Edward Castronova

Edward Castronova
is Associate Professor of Economics at California State University, Fullerton, USA.Author’s homepage

On Virtual Economies

by Edward Castronova

Abstract:

Currently, several million people have accounts in massively multiplayer online games. The population of virtual worlds has grown rapidly since 1996; significantly, each world also seems to grow its own economy, with production, assets and trade with Earth economies. This paper explores two questions about these developments. First, will these economies grow in importance? Second, if they do grow, how will that affect real-world economies and governments? To shed light on the first question, the paper presents a simple choice model of the demand for game time. The model reveals a certain puzzle about puzzles and games: in the demand for these kinds of interactive entertainment goods, people reveal that they are willing to pay money to be constrained. Still, the nature of games as a produced good suggests that technological advances, and heavy competition, will drive the future development of virtual worlds. If virtual worlds do become a large part of the daily life of humans, their development may have an impact on the macroeconomies of Earth. It will also raise certain constitutional issues, since it is not clear, today, exactly who has jurisdiction over these new economies.


<ahem>To claim that there is a problematic at work here in the juristictional supervision of these online entertainment services is specious, at best; and at least, willfully ignorant.</ahem>  Who owns the computers upon which they operate, and to whom is payment made to allow participation thereon?  These, at least, are the subjects of jurisdictional supervision.

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Using Smartphones for Collecting Behavioral Data in Psychological Science: Opportunities, Practical Considerations, and Challenges | Harari, Lane, Wang, Crosier, Campbell, Gosling

Gabriella M. Harari, Nicholas D. Lane, Rui Wang, Benjamin S. Crosier,
Andrew T. Campbell, Samuel D. Gosling; Using Smartphones for Collecting Behavioral Data in Psychological Science: Opportunities, Practical Considerations, and Challenges, draft; To appear in Perspectives in Psychological Science; WHEN?; 41 pages (double spaced); 2016-11-28; landing.

Abstract

Smartphones now offer the promise of collecting behavioral data unobtrusively, in situ, as it unfolds in the course of daily life. Data can be collected from the onboard sensors and other phone logs embedded in today’s off-the-shelf smartphone devices. These data permit fine- grained, continuous collection of people’s social interactions (e.g., speaking rates in conversation, size of social groups, calls, and text messages), daily activities (e.g., physical activity, sleep) and mobility patterns (e.g., frequency and duration of time spent at various locations). Here we draw on the lessons from the first wave of smartphone-sensing research to highlight areas of opportunity for psychological research, present practical considerations for designing smartphone studies, and discuss the ongoing methodological and ethical challenges associated with research in this domain. It is hoped that these practical guidelines will facilitate the use of smartphones as a behavioral observation tool in psychological science.

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The Dataflow Model: A Practical Approach to Balancing Correctness, Latency, and Cost in Massive-Scale, Unbounded, Out-of-Order Data Processing | Akidau et al. (Google)

Tyler Akidau, Robert Bradshaw, Craig Chambers, Slava Chernyak, Rafael J. Fernandez-Moctezuma, Reuven Lax, Sam McVeety, Daniel Mills, ́ Frances Perry, Eric Schmidt, Sam Whittle; The Dataflow Model: A Practical Approach to Balancing Correctness, Latency, and Cost in Massive-Scale, Unbounded, Out-of-Order Data Processing; In Proceedings of the Conference on Very Large Data Bases (VLDB), Volume 8, Number 12; 2015-08-31; 12 pages; Google, paywall

Abstract

Unbounded, unordered, global-scale datasets are increasingly common in day-to-day business (e.g. Web logs, mobile usage statistics, and sensor networks). At the same time, consumers of these datasets have evolved sophisticated requirements, such as event-time ordering and windowing by features of the data themselves, in addition to an insatiable hunger for faster answers. Meanwhile, practicality dictates that one can never fully optimize along all dimensions of correctness, latency, and cost for these types of input. As a result, data processing practitioners are left with the quandary of how to reconcile the tensions between these seemingly competing propositions, often resulting in disparate implementations and systems.

We propose that a fundamental shift of approach is necessary to deal with these evolved requirements in modern data processing. We as a field must stop trying to groom unbounded datasets into finite pools of information that even- tually become complete, and instead live and breathe under the assumption that we will never know if or when we have seen all of our data, only that new data will arrive, old data may be retracted, and the only way to make this problem tractable is via principled abstractions that allow the practitioner the choice of appropriate tradeoffs along the axes of interest: correctness, latency, and cost.

In this paper, we present one such approach, the Dataflow Mode , along with a detailed examination of the semantics it enables, an overview of the core principles that guided its design, and a validation of the model itself via the real-world experiences that led to its development

References

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Feelings of Discontent and the Promise of Middle Range Theory for STS | Geels

Frank W. Geels; Feelings of Discontent and the Promise of Middle Range Theory for Science & Technology Studies (STS); In Science, Technology & Human Values, Volume 32, Issue 6; 2007-11-01; DOI:10.1177/016224390303597; 25 pages; paywall
Teaser: Examples from Technology Dynamics

Abstract

This article critically discusses the state of STS, expressing feelings of discontent regarding four aspects: policy relevance, conceptual language, too much focus on complexity, theoretical styles. Middle range theory is proposed as an alternative, promising avenue. Middle range theories focus on delimited topics, make explicit efforts to combine concepts, and search for abstracted patterns and explanatory mechanisms. The article presents achievements in that direction for technology dynamics, particularly with regard to the role of expectations, niche theory and radical innovation, and the multi-level perspective on sociotechnical transitions.

Mentions

  • Middle Range Theory (MRT)
  • Science & Techology Studies (STS)
  • Merton introduced the notion of MRT in sociology in the three editions
    of Social Theory and Social Structure (1949, 1957, 1968).
    e.g.
    Merton, R.K. 1948. Discussion of Parsons’ `The position of sociological theory ‘. American Sociological Review 13(2): 164-168. Google Scholar

Aside

At the paywall, it is unclear who wrote the article.  The paywall declares that it was Frank W. Geels, but provides an “author biography” for Casper Bruun Jensen.

Yup, it is Frank W. Geels. Yet…

Casper Bruun Jensen is
  • Associate professor at the Technologies in Practice group, IT University of Copenhagen.
  • Casper Bruun Jensen, Ontologies for Developing Things (Sense, 2010)
  • Casper Bruun Jensen, Brit Ross Winthereik, Monitoring Movements in Development (MIT, 2013).

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Tracking the Digital Footprints of Personality | Lambiotte, Kosinski

Renaud Lambiotte, Michal Kosinski; Tracking the Digital Footprints of Personality; In Proceedings of the IEEE, Volume 102, Number 12; 2014-12; 5 pages; pdf (timeout? aclwalled?)
Teaser: This paper reviews literature showing how pervasive records of digital footprints can be used to infer personality.

Abstract

A growing portion of offline and online human activities leave digital footprints in electronic databases. Resulting big social data offers unprecedented insights into population-wide patterns and detailed characteristics of the individuals. The goal of this paper is to review the literature showing how pervasive records of digital footprints, such as Facebook profile, or mobile device logs, can be used to infer personality, a major psychological framework describing differences in individual behavior. We briefly introduce personality and present a range of works focusing on predicting it from digital footprints and conclude with a discussion of the implications of these results in terms of privacy, data ownership, and opportunities for future research in computational social science.

References

There are 54 references.

Network Diversity and Affect Dynamics: The Role of Personality Traits | Alshamsi, Pianesi, Lepri, Pentland, Rahwan

Aamena Alshamsi, Fabio Pianesi, Bruno Lepri, Alex Pentland, Iyad Rahwan; Network Diversity and Affect Dynamics: The Role of Personality Traits; In Public Library of Science | One (PLOS | One); 2016-04-01.DOI:10.1371/journal.pone.0152358

Abstract

People divide their time unequally among their social contacts due to time constraints and varying strength of relationships. It was found that high diversity of social communication, dividing time more evenly among social contacts, is correlated with economic well-being both at macro and micro levels. Besides economic well-being, it is not clear how the diversity of social communication is also associated with the two components of individuals’ subjective well-being, positive and negative affect. Specifically, positive affect and negative affect are two independent dimensions representing the experience (feeling) of emotions. In this paper, we investigate the relationship between the daily diversity of social communication and dynamic affect states that people experience in their daily lives. We collected two high-resolution datasets that capture affect scores via daily experience sampling surveys and social interaction through wearable sensing technologies: sociometric badges for face-to-face interaction and smart phones for mobile phone calls. We found that communication diversity correlates with desirable affect states–e.g. an increase in the positive affect state or a decrease in the negative affect state–for some personality types, but correlates with undesirable affect states for others. For example, diversity in phone calls is experienced as good by introverts, but bad by extroverts; diversity in face-to-face interaction is experienced as good by people who tend to be positive by nature (trait) but bad for people who tend to be not positive by nature. More broadly, the moderating effect of personality type on the relationship between diversity and affect was detected without any knowledge of the type of social tie or the content of communication. This provides further support for the power of unobtrusive sensing in understanding social dynamics, and in measuring the effect of potential interventions designed to improve well-being.

References

There are 57 refreences.

Users of the main smartphone operating systems (iOS, Android) differ only little in personality | Götz, Stieger, Reips

Friedrich M. Götz,, Stefan Stieger, Ulf-Dietrich Reips; Users of the main smartphone operating systems (iOS, Android) differ only little in personality; In Public Library of Science | One (PLOS | One); 2017-05-03; DOI:10.1371/journal.pone.0176921

Abstract

The increasingly widespread use of mobile phone applications (apps) as research tools and cost-effective means of vast data collection raises new methodological challenges. In recent years, it has become a common practice for scientists to design apps that run only on a single operating system, thereby excluding large numbers of users who use a different operating system. However, empirical evidence investigating any selection biases that might result thereof is scarce. Henceforth, we conducted two studies drawing from a large multi-national (Study 1; N = 1,081) and a German-speaking sample (Study 2; N = 2,438). As such Study 1 compared iOS and Android users across an array of key personality traits (i.e., well-being, self-esteem, willingness to take risks, optimism, pessimism, Dark Triad, and the Big Five). Focusing on Big Five personality traits in a broader scope, in addition to smartphone users, Study 2 also examined users of the main computer operating systems (i.e., Mac OS, Windows). In both studies, very few significant differences were found, all of which were of small or even tiny effect size mostly disappearing after sociodemographics had been controlled for. Taken together, minor differences in personality seem to exist, but they are of small to negligible effect size (ranging from OR = 0.919 to 1.344 (Study 1), ηp2 = .005 to .036 (Study 2), respectively) and may reflect differences in sociodemographic composition, rather than operating system of smartphone users.

References

There are 80 references.

The Sociology of Expectations in Science and Technology | Borup, Brown, Konrad, van Lente

Mads Borup, Nik Brown, Kornelia Konrad, Harro Van Lente; The Sociology of Expectations in Science and Technology; an Editorial; In Technology Analysis & Strategic Management, Volume 18, Numbers 3/4, 285 –298, July – September, 2006-07; 14 pages; DOI:10.1080/09537320600777002; paywall; copy

Relevance

Claim: Moore’s law is a self-fulfilling prophecy; by stating the law and the “tick tock” roadmap, the vision is driven to successful eventuality.

Mentions

  • “The Hype Cycle,” The Gartner Group
    The metaphoric device of an underdamped oscillator applied to social processes.
    Hype Cycle, In Jimi Wales’ Wiki.

Concept

<quote>By definition, innovation in contemporary science and technology is an intensely future-oriented business with an emphasis on the creation of new opportunities and capabilities. Novel technologies and fundamental changes in scientific principle do not substantively pre-exist themselves, except and only in terms of the imaginings, expectations and visions that have shaped their potential. As such, future-oriented abstractions are among the most important objects of enquiry for scholars and analysts of innovation. Such expectations can be seen to be fundamentally ‘generative’, they guide activities, provide structure and legitimation, attract interest and foster investment. They give definition to roles, clarify duties, offer some shared shape of what to expect and how to prepare for opportunities and risks. Visions drive technical and scientific activity, warranting the production of measurements, calculations, material tests, pilot projects and models. As such, very little in innovation can work in isolation from a highly dynamic and variegated body of future-oriented understandings about the future.</quote>

<ahem>future-oriented understandings about the future.</ahem>

References

There are 49 bibliographic references.

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  2. M. Michael, Futures of the present: from performativity to prehension, in: N. Brown, B. Rappert & A. Webster (Eds) Contested Futures: A Sociology of Prospective Techno-Science (Aldershot, UK, Ashgate, 2000).
  3. M. Sturken, D. Thomas & S. J. Ball-Rokeach (Eds), Technological Visions. The Hopes and Fears that Shape New Technologies (Philadelphia, PA, Temple University Press, 2004).
  4. N. Brown, B. Rappert & A. Webster (Eds), Contested Futures: A Sociology of Prospective Techno-Science (Aldershot, UK, Ashgate, 2000).
  5. W. Bijker & J. Law (Eds), Shaping Technology/Building Society (Cambridge, MA, MIT Press, 1992); A. Pickering (Ed.), Science as Practice and Culture (Chicago, IL, University of Chicago Press, 1992); B. Latour, Science in Action: How to Follow Scientists and Engineers through Society (Milton Keynes, UK, Open University Press, 1987); J. Law (Ed.), A Sociology of Monsters—Essays on Power, Techno- logy and Domination (London, Routledge, 1991).
  6. H. van Lente & A. Rip, Expectations in technological developments: an example of prospective structures to be filled by agency, in: C. Disco & B. van der Meulen (Eds), Getting New Technologies Together. Studies in Making Sociotechnical Order (Berlin, De Gruyter, 1998).
  7. J. Guice, Designing the future: the culture of new trends in science and technology, Research Policy, 28, 1999, pp. 81– 98.
  8. P. Martin, Great expectations: the construction of markets, products and user needs during the early development of gene therapy in the USA, in: R. Coombs, K. Green, A. Richards & V. Walsh (Eds), Technology and the Market: Demand, Users and Innovation (Cheltenham, UK, Edward Elgar, 2001); A. Hedgecoe & P. Martin, The drugs don’t work: expectations and the shaping of pharmacogenetics, Social Studies of Science, 33, 2003, pp. 327 –364.
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  10. H. Nowotny & U. Felt, After the Breakthrough—the Emergence of High-Temperature Superconductivity as a Research Field (Cambridge, UK, Cambridge University Press, 1997); M. Callon, Variety and irre- versibility in networks of technique conception and adoption, in: D. Foray & C. Freeman (Eds), Tech- nology and the Wealth of Nations—The Dynamics of Constructed Advantage (London, Pinter, 1993).
  11. Van Lente, op. cit., Ref. 1; Van Lente & Rip, op. cit., Ref. 6; J. Deuten & A. Rip, Narrative infrastructure in product creation processes, Organization, 7, 2000, pp. 69– 63; K. Konrad, Pra ̈gende Erwartungen— Szenarien als Schrittmacher der Technikentwicklung (Berlin, Edition Sigma, 2004).Editorial 297
  12. N. Brown & M. Michael, A sociology of expectations: retrospecting prospects and prospecting retro- spects, Technology Analysis and Strategic Management, 15, 2003, pp. 3–18.
  13. M. Dierkes, U. Hoffman & L. Maez, Leitbild und Technik: Zur Entstehung und Steuerung technischer Innovationen (Berlin, Edition Sigma, 1992); W. Rammert, Die kulturelle Orientierung der technischen Entwicklung. Eine technikgenetische Perspektive, in: D. Siefkes, P. Eulenho ̈fer, H. Stach & K. Sta ̈dtler, (Eds), Sozialgeschichte der Informatik. Soziale Praktiken und Orientierungen (Wiesbaden, Deutscher Universita ̈ts Verlag, 1998); H. D. Hellige, Technikleitbilder auf dem Pru ̈fstand: Leitbild-Assessment aus Sicht der Informatik- und Computergeschichte (Berlin, Edition Sigma, 1996).
  14. For example, M. Akrich, The de-scription of technical objects, in: Bijker & Law, op. cit., Ref 5, pp. 205– 224; W. B. Carlson, Artifacts and frames of meaning: Thomas A. Edison, his managers, and the cultural construction of motion pictures, in shaping technology/building society, in: Bijker & Law, op. cit., Ref 5; J. Jelsma, Innovating for sustainability: involving users, politics and technology, Innovation, 16, 2003, pp. 103–116; N. Oudshoorn & T. Pinch, How Users Matter: The Co-construction of Users and Technol- ogy (Cambridge, MA, MIT Press, 2003).
  15. B. De Laat, Scripts for the future: using innovation studies to design foresight tools, in: Brown et al., op. cit., Ref. 4; FORMAKIN, Final Report of the Formakin Project (Foresight as a Tool for the Management of Knowledge Flows and Innovation), York etc.: Science and Technology Studies Unit, University of York, 2001. An EU-TSERP project led by A.Webster, L. Sanz-Mene ́ndez and B. van der Meulen.
  16. C. Marvin, When Old Technologies were New (Oxford, Oxford University Press, 1990); M. Levin, When the Eiffel Tower was New: French Visions of Progress at the Centennial of the Revolution (Cambridge, MA, University of Massachusetts Press, 1989).
  17. Ibid.
  18. R. Kosellek, Futures Past—On the Semantics of Historical Time (Columbia, NY, Columbia University Press, 2004).
  19. M. Weber, Politics as a vocation, in: H. Gerth & C. W. Mills (Eds), From Max Weber: Essays in Sociology (London, Routledge and Kegan Paul, 1958), pp. 77– 128; G. H. Mead, The Philosophy of the Present (Chicago, IL, Chicago University Press, 1932); A. Schutz, On multiple realities, in: Collected Papers I, The Problem of Social Reality (The Hague, Alfred Schutz, 1962); A. Schutz, Tiresias, or our knowledge of future events, in: Collected Papers II, Studies in Social Theory (The Hague, Alfred Schutz, 1964); M. Emirbayer & A. Mische, What is agency?, American Journal of Sociology, 103(4), 1998, pp. 962 –1023.
  20. R. K. Merton, Socially expected durations: a case study of concept formation in sociology, in: W. Powell & R. Robbins (Eds), Conflict and Consensus: A Festschrift for L. Coser (New York, Free Press, 1984); B. Adam, Timescapes of Modernity: The Environment and Invisible Hazards (London, Routledge, 1998); B. Adam, Time and Social Theory (Cambridge, Polity, 1990); P. Virilio, The Information Bomb (London, Verso, 2000); P. Virilio, Speed and Politics (Columbia, NY, Columbia University Press, 1986).
  21. F. Bartlett, Remembering. A study in Experiential and Social Psychology (Cambridge, UK, Cambridge University Press, 1995); P. Jedlowski, Memory and sociology: themes and issues, Time and Society, 10, 2001, pp. 29– 44; M. Halbwacks, La Memoire Collective (Paris, Albin Michel, 1997).
  22. J. M. Barbalet, Social emotions: confidence, trust and loyalty, International Journal of Sociology and Social Policy, 16(9/10), 1996, pp. 75 –96.
  23. R. K. Merton, The self-fulfilling prophecy, The Antioch Review, 8, 1948, pp. 193– 210.
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  26. R. M. Grant, Contemporary Strategy Analysis, 2nd edn (Oxford, Blackwell, 1995).298 M. Borup et al.
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  28. R. Koppl, Big Players and the Economic Theory of Expectations (London, Palgrave, 2002); J. Pixley, Finance organisations, decisions and emotions, British Journal of Sociology, 53(1), 2002, pp. 41–65.
  29. De Laat, op. cit., Ref. 15; H.van Lente, From promises to requirement, in: Brown et al., op. cit., Ref. 4.
  30. Konrad, op. cit., Ref. 11; Van Lente, op. cit., Ref. 29.
  31. F. Geels & W. Smit, Lessons form failed technology futures: potholes in the road to the future’, in Ref 4, pp. 881–882.
  32. Ibid.
  33. N. Luhmann, The modernity of science, New German Critique, 61, Winter 1994, pp. 9–16.
  34. Kosellek, op. cit., Ref. 18.
  35. J. Mokyr, Evolutionary biology, technological change and economic history, Bulletin of Economic Research, 43(2), 1991, pp. 127– 149.
  36. N. Brown, Hope against hype: accountability in biopasts, presents and futures, Science Studies, 16(2), 2003, pp. 3–21.
  37. Deuten & Rip, op. cit., Ref. 11.
  38. Konrad, op. cit., Ref. 11; Brown & Michael, op. cit., Ref. 12.
  39. Van Lente, op. cit., Ref. 29.
  40. W. Bijker, Of Bicycles, Bakelites, and Bulps—Toward a Theory of Sociotechnical Change (Cambridge, MA, MIT Press, 1995), ch. 5.
  41. Brown & Michael, op. cit., Ref. 12.
  42. D. MacKenzie, Inventing Accuracy: A Historical Sociology of Nuclear Missile Guidance (Cambridge, MA, MIT Press, 1990).
  43. J. Ravetz, What is post-normal science?, Futures, 31, 1999, pp. 647– 653.
  44. Van Lente, op. cit., Ref. 1; Konrad, this issue.
  45. Michael, op. cit., Ref. 2.
  46. C. Thompson, The biotech mode of reproduction, Paper prepared for the School of American Research Advanced Seminar ‘Animation and Cessation: Anthropological Perspectives on Changing Definitions of Life and Death in the Context of Biomedicine’, Santa Fe, New Mexico, 2000.
  47. P. Weingart, A. Engels & P. Pansegrau, Risks of communication: discourses on climate change in science, politics, and mass medi, Public Understanding of Science, 9(3), 2000, pp. 261–283.
  48. H. Nowotny, P. Scott & M. Gibbons, Re-thinking Science—Knowledge and the Public in an Age of Uncertainty (Cambridge, UK, Polity Press, 2001), p. 232.
  49. Brown et al., op. cit., Ref. 4.

De-Anonymizing Web Browsing Data with Social Networks | Su, Shukla, Goel, Narayanan

Jessica Su, Ansh Shukla, Sharad Goel, Arvind Narayanan; De-Anonymizing Web Browsing Data with Social Networks; draft; In Some Venue Surely (they will publish this somewhere, it is so very nicely formatted); 2017-05; 9 pages.

Abstract

Can online trackers and network adversaries de-anonymize web browsing data readily available to them? We show—theoretically, via simulation, and through experiments on real user data—that de-identified web browsing histories can be linked to social media profiles using only publicly available data. Our approach is based on a simple observation: each person has a distinctive social network, and thus the set of links appearing in one’s feed is unique. Assuming users visit links in their feed with higher probability than a random user, browsing histories contain tell-tale marks of identity. We formalize this intuition by specifying a model of web browsing behavior and then deriving the maximum likelihood estimate of a user’s social profile. We evaluate this strategy on simulated browsing histories, and show that given a history with 30 links originating from Twitter, we can deduce the corresponding Twitter profile more than 50% of the time. To gauge the real-world effectiveness of this approach, we recruited nearly 400 people to donate their web browsing histories, and we were able to correctly identify more than 70% of them. We further show that several online trackers are embedded on sufficiently many websites to carry out this attack with high accuracy. Our theoretical contribution applies to any type of transactional data and is robust to noisy observations, generalizing a wide range of previous de-anonymization attacks. Finally, since our attack attempts to find the correct Twitter profile out of over 300 million candidates, it is—to our knowledge—the largest-scale demonstrated de-anonymization to date.

Promotions

  • Ad Networks Can Personally Identify Web Users; Wendy Davis; In MediaPost; 2017-01-20.
    <quote> The authors tested their theory by recruiting 400 people who allowed their Web browsing histories to be tracked, and then comparing the sites they visited to sites mentioned in Twitter accounts they followed. The researchers say they were able to use that method to identify more than 70% of the volunteers.</quote>