Privacy Targets: Two User Studies on Internet Privacy and Targeted Advertising
Google Tech Talk
June 1, 2010
Presented by Aleecia McDonald.
Targeted advertising, including behavioral adverting, collects data about an individual's online activities for use in selecting which advertisement to display. Targeted ads have enjoyed commercial success, and have the potential to reduce costs to advertisers while increasing relevance to consumers. However, questions about consumer's online privacy are at issue, and there is both regulatory and legislative interest.
Aleecia M. McDonald will discuss findings from two recent studies about online privacy and targeted advertisement performed at Carnegie Mellon's CUPS lab. The first was a laboratory study with hour long in-depth qualitative interviews with 14 subjects. Participants held a wide range of views from enthusiasm about ads that inform them of new products to resentment of ads that they find invasive. Some people are not even aware of when they are being advertised to, let alone aware of what data is collected or how it is used. We found limited understanding of technologies used in targeted advertising and confusion between cookies and history. Second, we performed an online survey of 300 participants in which we contrasted participants' views about behavioral advertising to other popular types of advertising (contextual, affiliate, cloud-based, and DPI- based.) Participants reported they would rather see random ads than all other forms of advertisement with the exception of contextual ads. We asked participants to imagine a trade off of spending a dollar per month to keep data private on a website they frequent, or keep content free but have data used in behavioral advertisement. We found many reasons people self-report they would or would not pay for privacy, including a subset of people who are very privacy protective but would not pay because they feel it is wrong to pay for privacy. Discussion will conclude with suggestions for policy makers and technologists.
Aleecia M. McDonald is a PhD candidate in Engineering & Public Policy at Carnegie Mellon University, where she is a member of the Cylab Usable Privacy and Security (CUPS) research laboratory. Her interests span the intersection of Internet technology, policy, economics, and law. Ms. McDonald's research includes the efficacy of industry self regulation, behavioral economics and mental models of privacy, network traffic analysis to combat spyware, automotive privacy, and RFID technology. In addition to a decade of experience working for software startups, Ms. McDonald holds an MS in Public Policy and Management, and a BA in Professional Writing, both from Carnegie Mellon. Her findings have been featured in media outlets such as the Washington Post, Ars Technica, Free Press' Media Minute, and have contributed to testimony before the Federal Trade Commission.
Aleecia has many publications to her name (http://www.aleecia.com/cv.html), and has presented at Google before (Online Privacy: Industry and Self-regulation in practice: http://www.youtube.com/watch?v=BNO7Q5_o4RY), when she visited last year.