Susan Athey, Christian Catalini, Catherine Tucker; The Digital Privacy Paradox: Small Money, Small Costs, Small Talk; working paper; 2017-02-13; 32 pages; landing; Working Paper W23488; National Bureau o Gratuitously Paywall Rough Drafts (NBER); paywall.
Susan Athey, senior fellow, Stanford Institute for Economic Policy Research
Christian Catalini, MIT
Catherine Tucker, MIT
tl;dr → <quote>Consumers say they care about privacy, but at multiple points in the process they end up making choices that are inconsistent with their stated preferences.</quote>
See Item #3, How cool of a result is that? You are safe, you are loved, you are subtle, you are special. You may opt out any time. And we give back to the community.
This paper uses data from the MIT digital currency experiment to shed light on consumer behavior regarding commercial, public and government surveillance. The set- ting allows us to explore the apparent contradiction that many cryptocurrencies offer people the chance to escape government surveillance, but do so by making transactions themselves public on a distributed ledger (a ‘blockchain’). We find three main things.
- First, the effect of small incentives may explain the privacy paradox, where people say they care about privacy but are willing to relinquish private data quite easily.
- Second, small costs introduced during the selection of digital wallets by the random ordering of featured options, have a tangible effect on the technology ultimately adopted, often in sharp contrast with individual stated preferences about privacy.
- Third, the introduction of irrelevant, but reassuring information about privacy protection makes consumers less likely to avoid surveillance at large.
- Acquisti, A., C. Taylor, and L. Wagman (2016). The economics of privacy. In Journal of Economic Literature 54 (2), 442–492.
- Allcott, H. and T. Rogers (2014). The short-run and long-run effects of behavioral interventions: Experimental evidence from energy conservation. In The American Economic Review 104 (10), 3003–3037.
- Athey, S., I. Parashkevov, S. Sarukkai, and J. Xia (2016). Bitcoin pricing, adoption, and usage: Theory and evidence. SSRN Working Paper ssrn:2822729.
- Barnes, S. B. (2006). A Privacy Paradox: Social Networking in the United States. In First Monday 11 (9).
- Bertrand, M., D. Karlan, S. Mullainathan, E. Shafir, and J. Zinman (2010). What’s advertising content worth? evidence from a consumer credit marketing field experiment. In The Quarterly Journal of Economics 125 (1), 263–306.
- Catalini, C. and J. S. Gans (2016). Some simple economics of the blockchain. SSRN Working Paper No. ssrn:2874598.
- Catalini, C. and C. Tucker (2016). Seeding the s-curve? The role of early adopters in diffusion. SSRN Working Paper No. 28266749,
- Chetty, R., A. Looney, and K. Kroft (2009). Salience and taxation: Theory and evidence. In The American Economic Review 99 (4), 1145–1177.
- DellaVigna, S. (2009). Psychology and economics: Evidence from the field. In Journal of Economic Literature 47 (2), 315–372.
- DellaVigna, S., J. A. List, and U. Malmendier (2012). Testing for altruism and social pressure in charitable giving. In The Quarterly Journal of Economics, qjr050.
- DellaVigna, S. and U. Malmendier (2006). Paying not to go to the gym. The American Economic Review 96 (3), 694–719.
- Gneezy, U. and J. A. List (2006). Putting behavioral economics to work: Testing for gift exchange in labor markets using field experiments. In Econometrica 74 (5), 1365–1384.
- Greenstein, S. M., J. Lerner, and S. Stern (2010). The economics of digitization: An agenda for the National Science Foundation (NSF).
- Gross, R. and A. Acquisti (2005). Information revelation and privacy in online social networks. In Proceedings of the 2005 ACM Workshop on Privacy in the Electronic Society (WPES ’05), New York, NY, USA, pp. 71–80. ACM: ACM.
- Harrison, G. W. and J. A. List (2004). Field experiments. In Journal of Economic Literature 42 (4), 1009–1055.
- Ho, D. E. and K. Imai (2008). Estimating causal effects of ballot order from a randomized natural experiment the california alphabet lottery, 1978–2002. In Public Opinion Quarterly 72 (2), 216–240.
- Kim, J.-H. and L. Wagman (2015). Screening incentives and privacy protection in financial markets: a theoretical and empirical analysis. In The RAND Journal of Economics 46 (1), 1–22.
- Landry, C. E., A. Lange, J. A. List, M. K. Price, and N. G. Rupp (2010). Is a donor in hand better than two in the bush? evidence from a natural field experiment. In The American Economic Review 100 (3), 958–983.
- Madrian, B. C. and D. F. Shea (2001). The power of suggestion: Inertia in 401 (k) participation and savings behavior. In The Quarterly Journal of Economics 116 (4), 1149–1187.
- Marthews, A. and C. Tucker (2015). Government Surveillance and Internet Search Behavior SSRN Working Paper No. ssrn:2412564,
- Miller, A. and C. Tucker (2011). Can healthcare information technology save babies? In Journal of Political Economy (2), 289–324.
- Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system.
- Narayanan, A., J. Bonneau, E. Felten, A. Miller, and S. Goldfeder (2016). Bitcoin Cryptocurrency Technologies. Princeton University Press: Princeton NJ.
- Posner, R. A. (1981). The economics of privacy. In The American Economic Review 71 (2), 405–409.
Eric Rodenbeck (Stamen Design); Big Glass Microphone is a data visualization of a 5km long fiber optic cable buried underneath Stanford University; In Their Blog, hosted on Medium; 2017-05-15.
Big Glass Microphone, hosted at Stamen Design.
- Big Glass Microphone
- Distributed Acoustic Sensing technology
along rail rights-of-way
- 0–0.6hz, 10–20hz and so on, up to 320 Hertz.
- 85 to 180 Hz → male voice
- 165 to 255 Hz. → female voice
<quote>Big Glass Microphone is intended to evoke a sense of wonder about the kinds of detections and interactions that are increasingly common in our uniquitously networked society</quote>
Teaser: Java’s days are numbered – but it’s a very large number
CS department updates introductory courses; Stephanie Brito; In The Stanford Daily; 2017-02-28.
- Eric Roberts, Emeritus Professor of Computer Science, Stanford University
- CS 106A, The Art & Science of Java.
- Stephen O’Grady, co-founder, RedMonk
Can you picture the three most important technologies in your life twenty years from today? Could you tell a vivid story about the single biggest challenge you’ll personally face five years from now? What about the biggest challenge the world will face in fifty years? Thinking about the far-off future isn’t just an exercise in intellectual curiosity. It’s a practical skill that, new research reveals, has a direct neurological link to greater creativity, empathy, and optimism. In other words, futurist thinking gives you the ability to create change in your own life and the world around you, today.In this course, you’ll learn essential habits for thinking about the future that will increase the power of your practical imagination. These futurist habits include counterfactual thinking (imagining how the past could have turned out differently); signals hunting (looking for leading-edge examples of the kind of change you want to see in the world); and autobiographical forecasting. We’ll discuss the scientific research that explains how each habit can have a positive impact on your life, from helping you become a more original thinker to making you a more persuasive communicator. By the end of this course, you will have the playful and practical tools you need to imagine how the world (and your life) could be very different—and to use your newfound imagination to create change today.
Jane McGonigal, Director of Games Research and Development, Institute for the Future
Jane McGonigal created forecasting games for partners like the World Bank, the Rockefeller Foundation, the New York Public Library, and the American Heart Association. Well known for her TED talks on creativity and resilience, she is the author of two New York Times bestselling books, Reality Is Broken and SuperBetter. She received a PhD in performance studies from UC Berkeley.
- Kevin Kelly, The Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future, ISBN:978-0143110378,
- Jane McGonigal, Reality is Broken, ISBN:978-0143120612,
- Rebecca Solnit, Hope in the Dark: Untold Histories, Wild Possibilities, ISBN:1608465764
- Kevin Kelly, The Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future, ISBN:978-0143110378, paperback: 2017-06-06.
- Jane McGonigal, Reality is Broken, ISBN:978-0143120612,
- Rebecca Solnit, Hope in the Dark: Untold Histories, Wild Possibilities, ISBN 1608465764,
Followup herein and in the notes.
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.
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.
- 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>
‘Design Thinking’ for a Better You; Tara Parker-Pope; In The New York Times (NYT); 2016-01-04.
Bernard Roth; The Achievement Habit: Stop Wishing, Start Doing, and Take Command of Your Life; HarperBusiness; 2015-07-07; 288 pages; kindle: $13, paper: $15+SHT.
- Bernard Roth
- professor, engineering, Stanford
- a founder, Hasso Plattner Institute of Design at Stanford
- The Achievement Habit
i.e. requirements extraction
- “define the problem”
i.e. scope it, limit it.
i.e. develop the set of alternatives; e.g. brainstorm, make lists, write down ideas, generate possible solutions.
- prototype or plan (as appropriate)
- test, get feedback
- The article reframes the method away from engineering towards social success
- finding a spouse (getting a date),
- weight loss
- self-acceptance (of weight that will not be lost).
Greg Orr; Diffusion of Innovations, by Everett Rogers (1995); class, Symbolic Systems 205; Stanford University; 2003-03-18.
Reviews some other edition of the book; 1st edition was 1962.