We investigate the feasibility of combining publicly available Web 2.0 data with off-the-shelf face recognition software for the purpose of large-scale, automated individual re-identification. Two ...experiments illustrate the ability of identifying strangers online (on a dating site where individuals protect their identities by using pseudonyms) and offline (in a public space), based on photos made publicly available on a social network site. A third proof-of-concept experiment illustrates the ability of inferring strangers' personal or sensitive information (their interests and Social Security numbers) from their faces, by combining face recognition, data mining algorithms, and statistical re-identification techniques. The results highlight the implications of the convergence of face recognition technology and increasing online self-disclosure, and the emergence of "personally predictable'' information, or PPI. They raise questions about the future of privacy in an "augmented'' reality world in which online and offline data will seamlessly blend.
Participation in social networking sites has dramatically increased in recent years. Services such as Friendster, Tribe, or the Facebook allow millions of individuals to create online profiles and ...share personal information with vast networks of friends - and, often, unknown numbers of strangers. In this paper we study patterns of information revelation in online social networks and their privacy implications. We analyze the online behavior of more than 4,000 Carnegie Mellon University students who have joined a popular social networking site catered to colleges. We evaluate the amount of information they disclose and study their usage of the site's privacy settings. We highlight potential attacks on various aspects of their privacy, and we show that only a minimal percentage of users changes the highly permeable privacy preferences.
In recent years, progresses in data mining and business analytics have fostered the advent of recommender systems, behavioral advertising, and other ways of using consumer data to personalize offers ...and products. We investigate the incentives for sellers to invest in systems that allow the tracking of consumers and then to truthfully report whether potential buyers will enjoy yet untried products. We find that there are two types of equilibria: For some parameter values, sellers will target all potential buyers, hence their targeted ads or purchase recommendations provide no benefit to the consumer. But for other values, ads and recommendations will be accurate. In particular, the incentive for the seller to provide accurate ads and recommendations will be inversely related to the difference between the cost of producing the good and its average market evaluation.
Personal data is increasingly conceived as a tradable asset. Markets for personal information are emerging and new ways of valuating individuals’ data are being proposed. At the same time, legal ...obligations over protection of personal data and individuals’ concerns over its privacy persist. This article outlines some of the economic, technical, social, and ethical issues associated with personal data markets, focusing on the privacy challenges they raise.
"Heads or Tails?"-A Reachability Bias in Binary Choice Bar-Hillel, Maya; Peer, Eyal; Acquisti, Alessandro
Journal of experimental psychology. Learning, memory, and cognition,
11/2014, Volume:
40, Issue:
6
Journal Article
Peer reviewed
Open access
When asked to mentally simulate coin tosses, people generate sequences that differ systematically from those generated by fair coins. It has been rarely noted that this divergence is apparent already ...in the very 1st mental toss. Analysis of several existing data sets reveals that about 80% of respondents start their sequence with Heads. We attributed this to the linguistic convention describing coin toss outcomes as "Heads or Tails," not vice versa. However, our subsequent experiments found the "first-toss" bias reversible under minor changes in the experimental setup, such as mentioning Tails before Heads in the instructions. We offer a comprehensive account in terms of a novel response bias, which we call reachability. It is more general than the 1st-toss bias, and it reflects the relative ease of reaching 1 option compared to its alternative in any binary choice context. When faced with a choice between 2 options (e.g., Heads and Tails, when "tossing" mental coins), whichever of the 2 is presented first by the choice architecture (hence, is more reachable) will be favored. This bias has far-reaching implications extending well beyond the context of randomness cognition; in particular, to binary surveys (e.g., accept vs. reject) and tests (e.g., True-False). In binary choice, there is an advantage to what presents first.
•Randomized response techniques (RRTs) are designed to facilitate disclosure.•However, 9 studies show that RRTs can suppress, rather than increase, disclosure.•The paradox is driven by respondents’ ...concern over response misinterpretation.•The paradox is mitigated when concern over response misinterpretation is addressed.•We therefore show when and why RRTS are prone to backfiring.
By adding random noise to individual responses, randomized response techniques (RRTs) are intended to enhance privacy protection and encourage honest disclosure of sensitive information. Empirical findings on their success in doing so are, however, mixed. In nine experiments, we show that the noise introduced by RRTs can make respondents concerned that innocuous responses will be interpreted as admissions, and as a result, yield prevalence estimates that are lower than direct questioning (Studies 1–4, 5A, & 6), less accurate than direct questioning (Studies 1, 3, 4B, & 5A), and even nonsensical (i.e., negative; Studies 3–6). Studies 2A and 2B show that the paradox is eliminated when the target behavior is socially desirable, even when it is merely framed as such. Study 3 shows the paradox is driven by respondents’ concerns over response misinterpretation. A simple modification designed to reduce concerns over response misinterpretation reduces the problem (Studies 4 & 5), particularly when such concerns are heightened (Studies 5 & 6).
Privacy scholars have long studied, and argued about, a so-called privacy paradox---the alleged gap between individuals' claims of caring about privacy and their actual behaviors. This manuscript ...explores whether a different type of mismatch occurs in an online sample of US participants: a mismatch between participants' dismissive perspectives on privacy and their privacy-protective behaviors. In a series of online studies with Prolific US participants we tackle two research questions: is there evidence of mismatches between (dismissive) privacy perspectives, and (protective) privacy behaviors? If so, what can explain those mismatches? In a Behavior Elicitation study, we collect a corpus of privacy-regulating and privacy-protective behaviors. Next, in Study 1, we find evidence that engagement in a broad array of privacy behaviors is, in fact, very common in our sample. We also find that mismatches between dismissive privacy perspectives and protective behaviors emerge in a large proportion of participants. Finally, in Study 2, we uncover several common but distinct reasons for those mismatches, including construing seemingly protective behaviors as motivated by reasons other than privacy, and nuanced stances on when to express privacy concern. Collectively, the results indicate that individuals who are seemingly dismissive of privacy concerns engage in behaviors that can be construed as privacy-seeking. The findings highlight the nuances of individual privacy decision-making and suggest that public policy related to privacy should account for the evidence for widespread privacy-seeking behaviors.
Teaching Johnny not to fall for phish Kumaraguru, Ponnurangam; Sheng, Steve; Acquisti, Alessandro ...
ACM transactions on Internet technology,
05/2010, Volume:
10, Issue:
2
Journal Article
Peer reviewed
Open access
Phishing attacks, in which criminals lure Internet users to Web sites that spoof legitimate Web sites, are occurring with increasing frequency and are causing considerable harm to victims. While a ...great deal of effort has been devoted to solving the phishing problem by prevention and detection of phishing emails and phishing Web sites, little research has been done in the area of training users to recognize those attacks. Our research focuses on educating users about phishing and helping them make better trust decisions. We identified a number of challenges for end-user security education in general and anti-phishing education in particular: users are not motivated to learn about security; for most users, security is a secondary task; it is difficult to teach people to identify security threats without also increasing their tendency to misjudge nonthreats as threats. Keeping these challenges in mind, we developed an email-based anti-phishing education system called “PhishGuru” and an online game called “Anti-Phishing Phil” that teaches users how to use cues in URLs to avoid falling for phishing attacks. We applied learning science instructional principles in the design of PhishGuru and Anti-Phishing Phil. In this article we present the results of PhishGuru and Anti-Phishing Phil user studies that demonstrate the effectiveness of these tools. Our results suggest that, while automated detection systems should be used as the first line of defense against phishing attacks, user education offers a complementary approach to help people better recognize fraudulent emails and websites.
Over the past decade, social network sites have experienced dramatic growth in popularity, reaching most demographics and providing new opportunities for interaction and socialization. Through this ...growth, users have been challenged to manage novel privacy concerns and balance nuanced trade-offs between disclosing and withholding personal information. To date, however, no study has documented how privacy and disclosure evolved on social network sites over an extended period of time. In this manuscript we use profile data from a longitudinal panel of 5,076 Facebook users to understand how their privacy and disclosure behavior changed between 2005---the early days of the network---and 2011. Our analysis highlights three contrasting trends. First, over time Facebook users in our dataset exhibited increasingly privacy-seeking behavior, progressively decreasing the amount of personal data shared publicly with unconnected profiles in the same network. However, and second, changes implemented by Facebook near the end of the period of time under our observation arrested or in some cases inverted that trend. Third, the amount and scope of personal information that Facebook users revealed privately to other connected profiles actually increased over time---and because of that, so did disclosures to ``silent listeners'' on the network: Facebook itself, third-party apps, and (indirectly) advertisers. These findings highlight the tension between privacy choices as expressions of individual subjective preferences, and the role of the environment in shaping those choices.
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