How much can be inferred about user activity?
Speaker: Prof. Mark Crovella, Boston University
Abstract: In this talk I will discuss two recent projects. Both projects examine how well we can "guess" what network users are doing or will do.
In the first project, we ask: can an ISP infer how much business other ISPs are doing with each other? Specifically, can an ISP infer how much traffic flows between some other ISP (eg, a competitor) and its customers, even though that traffic is not directly measurable by the ISP? We show evidence that this can be done, at least in some cases. This suggests that information traditionally considered private (volumes of traffic exchanged) may not be as private as assumed.
In the second project, we ask: can one predict the future popularity of social media? We examine a large corpus of YouTube traces, and ask whether regularities exist that can form the basis for prediction of future activity. While some other work has suggested simple models for video popularity, we show that our data suggests a more complex situation. However, once the more sophisticated models are understood it becomes possible to do reasonable prediction of future video popularity.
Speaker's Bio: Mark Crovella is Professor of Computer Science at Boston University, where he has been since 1994. During 2003-2004 he was a Visiting Associate Professor at the Laboratoire d'Infomatique de Paris VI (LIP6). His research interests are in performance evaluation, focusing on parallel and networked computer systems. In the networking arena, he has worked on characterizing the Internet and the World Wide Web. He has explored the presence and implications of self-similarity and heavy-tailed distributions in network traffic and Web workloads. He has also investigated the implications of Web workloads for the design of scalable and cost-effective Web servers. In addition he has made numerous contributions to Internet measurement and modeling; and he has examined the impact of network properties on the design of protocols and the construction of statistical models. Professor Crovella is co-author of Internet Measurement: Infrastructure, Traffic, and Applications (Wiley Press, 2006) and is the author of over one hundred papers on networking and computer systems. He has served as Chair of ACM SIGCOMM, and he is a Fellow of the ACM.
Title: A Corporate View of Social Network Privacy Issues
Speaker: Mr. Marden Neubert, Universo Online; Mr. Nelson Novaes Neto, Universo Online and PUC-SP
Abstract: We discuss issues related to privacy in social networks from a corporate point of view. Social networks such as LinkedIn and Twitter are often used by individuals to communicate both in a personal and in a professional level. We explore problems that arise when those lines are crossed. We also discuss how interactions in social networks can be harmful to professionals as they might expose intentions of career moves. Such interactions can also be damaging for the enterprises involved as they might reveal strategic decisions.
Internet Privacy: Who gathers data and how, and what can be done about it
Speaker: Dr. Balachander Krishnamurthy, AT&T Labs
Abstract: For the last few years we have been examining the leakage of privacy on the Internet: how information related to individual users is aggregated as they browse seemingly unrelated Web sites. Thousands of Web sites across numerous categories, countries, and languages were studied to generate a privacy "footprint". I will report on our longitudinal study consisting of multiple snapshots of our examination of such diffusion over six years. We examine the various technical ways by which third-party aggregators acquire data and the depth of user-relate information acquired. Our results show increasing aggregation of user-related data by a steadily decreasing number of entities. I will present information on leakage of personally identifiable information (PII) via Online Social Networks (both traditiona and mobile OSNs). I will discuss various options on what can be done about this serious problem.
Speaker's Bio: Balachander Krishnamurthy is a member of technical staff at AT&T Labs--Research. His main focus of research of late is in the areas of Internet privacy, Online Social Networks, and Internet measurements. He has authored and edited ten books, published over 80 technical papers, holds twenty six patents, and has given invited talks in over thirty countries. He co-founded the successful Internet Measurement Conference and the Workshop on Online Social Networks. He has been on the thesis committee of several PhD students, collaborated with over seventy five researchers worldwide, and given tutorials at several industrial sites and conferences.
His most recent book "Internet Measurements: Infrastructure, Traffic and Applications" (525pp, Wiley, with Mark Crovella), was published in July 2006 and is the first book focusing on Internet Measurement. His previous book 'Web Protocols and Practice: HTTP/1.1, Networking Protocols, Caching, and Traffic Measurement' (672 pp, Addison-Wesley, with Jennifer Rexford) is the first in-depth book on the technology underlying the World Wide Web, and has been translated into Portuguese, Japanese, Russian, and Chinese.
Bala is homepageless and not on any OSN but many of his papers can be found at http://www.research.att.com/~bala/papers
Why do people unfollow in Twitter?
Speaker: Prof. Sue Moon, KAIST
Abstract: Twitter has attracted many social network researchers due to its popularity and unique characteristics. Accessibility to a large volume of data has made a variety of research areas available, including community structures, information propagation, and user ranking, as well as relationship evolution. However, while extensive work has done to understand how the friendship formulates in Twitter, the dissolution of friendship still has been unveiled.
We explore the dynamics in unfollow behavior in Twitter. We collected daily snapshots of follow relationship of 1:2 million Korean-speaking users for 51 days and their all tweets. From careful statistical analysis, we confirm that unfollow is prevalent and irrelevant to the volume of interaction. We find that other factors such as link reciprocity, tweet burstiness and informativeness are crucial for unfollow decision. We conduct interview with 22 users to supplement the results and figure out motivations behind unfollow behavior. From those quantitative and qualitative research we draw significant implications in both theory and practice.
Speaker's Bio: Sue Moon received her B.S. and M.S. from Seoul National University, Seoul, Korea, in 1988 and 1990, respectively, all in computer engineering. She received a Ph.D. degree in computer science from the University of Massachusetts at Amherst in 2000. From 1999 to 2003, she worked in the IPMON project at Sprint ATL in Burlingame, California. In August of 2003, she joined KAIST and now teaches in Daejeon, Korea.
She has served as TPC co-chair for ACM Multimedia 2004 and ACM SIGCOMM MobiArch Workshop 2007, general chair for PAM 2009, and TPC for many conferences, including SIGCOMM 2010, NSDI 2008, WWW 2007-2008, 2011, INFOCOM 2004-2006, and IMC 2009. She is currently serving on the editorial board of ACM SIGCOMM Computer Communications Review. She won the best paper award in ACM SIGCOMM Internet Measurement Conference 2007 and the best poster in ACM SIGCOMM 2010, and has been awarded the Amore Pacific Award for Outstanding Women in the Sciences in 2009. Recently she has been elected to the Korean Academy of Science and Technology as a junior member. Her research interests are: network performance measurement and analysis, online social networks, and networking systems.
Title: Promoting location privacy… one lie at a time
Speaker: Dr. Daniele Quercia
Abstract: Nowadays companies increasingly aggregate location data from different sources on the Internet to offer location-based services such as estimating current road traffic conditions, and finding the best nightlife locations in a city. However, these services have also caused outcries over privacy issues. As the volume of location data being aggregated expands, the comfort of sharing one's whereabouts with the public at large will unavoidably decrease. Existing ways of aggregating location data in the privacy literature are largely centralized in that they rely on a trusted location-based service. Instead, we propose a piece of software (SpotME) that can run on a mobile phone and allows privacy-conscious users of location-based services to report, in addition to their actual locations, also some erroneous locations. The erroneous locations are selected by a randomized response algorithm in a way that makes it possible to accurately collect and process aggregated location data without affecting the fidelity of the result. We evaluate the accuracy of SpotME in estimating the number of people in a certain location upon two very different realistic mobility traces: the mobility of vehicles in urban, suburban and rural areas, and the mobility of subway train passengers in Greater London. We find that erroneous locations have little effect on the estimations (in both traces, the error is below 18% for a situation in which more than 99% of the locations are erroneous), yet they guarantee that users cannot be localized with high probability. Also, the computational and storage overheads for a mobile phone running SpotME are negligible, and the communication overhead is limited (SpotME adds an overhead of 21 byte/s).