class: center, middle, inverse, title-slide .title[ # Privacy in Online Social Networks ] .author[ ### David Garcia <br> <em>ETH Zurich</em> ] .date[ ### Social Data Science ] --- layout: true <div class="my-footer"><span> <a href=https://advances.sciencemag.org/content/3/8/e1701172.full> Leaking privacy and shadow profiles in online social networks. David Garcia. Science Advances (2017) </a></span></div> --- ## Facebook likes predict private attributes <img src="figures/F1.large.jpg" width="900" style="display: block; margin: auto;" /> Private traits and attributes are predictable from digital records of human behavior [(Kosinski et al, 2013)](https://www.pnas.org/content/110/15/5802) --- ## Facebook likes predict private attributes  [Computer-based personality judgments are more accurate than those made by humans. Youyou et al, PNAS, 2015](https://www.pnas.org/doi/10.1073/pnas.1418680112) --- # The Cambridge Analytica Scandal <img src="figures/CA.jpeg" width="750" style="display: block; margin: auto;" /> https://www.ft.com/content/e325e3d0-2c3b-11e8-a34a-7e7563b0b0f4 --- # Privacy Risks in Online Platforms - A risk for informational self-determination: Private traits and attributes are predictable from digital records of human behavior [(Kosinski et al, 2013)](https://www.pnas.org/content/110/15/5802) - Facebook Likes predict personality, sexual orientation, etc Individualized solutions to privacy risks > ”Providing users with transparency and control over their information, leading to an individually controlled balance between the promises and perils of the Digital Age.” *(Kosinski et al, 2013)* <center> **Can we guarantee individual control over online privacy?** --- background-size: 20% background-image: url(figures/puzzle.jpg) background-position: 98% 70% ## The Problem of Shadow Profiles **Facebook Shadow Profile:** A file that Facebook keeps on you containing data it pulls from looking at the information that your friends voluntarily provide. [*(Digital Trends, 2013)*](https://www.digitaltrends.com/social-media/what-exactly-is-a-facebook-shadow-profile/) - 2011: Europe-vs-Facebook files complaint on shadow profiles - 2013: Bug reveals private information of 6 million users - Some users notice their shadow profiles with mobile numbers - Apr 2014: 1B users of Facebook for mobile share contact lists - Aug 2016: Whatsapp and Facebook share information Not a problem unique to Facebook: also in Twitter, Google, Amazon... --- ## Auditing Shadow Profiles **Shadow profile hypothesis:** The data shared by the users of an online platform predicts of personal information of non-users <img src="figures/shadowidea.png" width="600" style="display: block; margin: auto;" /> **Approach:** historical audit to evaluate how social networking sites could have predicted information of individuals who were not users yet <div style="font-size:15pt"> [One Plus One Makes Three (for Social Networks). Emöke-Ágnes Horvát, Michael Hanselmann, Fred Hamprecht, Katharina Zweig. Plos ONE (2012)](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0034740) --- ## Study 1: Friendster .pull-left[ <img src="figures/FriendsterLogo.png" width="550" style="display: block; margin: auto;" /> - Internet Archive dataset - Full track of publicly available data - Focus on first 20 million users - 3M profiles with public information - Profiles contain gender, marital status, and relationship interests ] .pull-right[ <img src="figures/FriendsterNet.png" width="450" style="display: block; margin: auto;" /> ] --- ## Assortativity of sensitive features in Friendster <img src="figures/Radars.png" width="870" style="display: block; margin: auto;" /> --- ## Unsupervised prediction based on friends <br> <img src="figures/ROCs.png" width="1100" style="display: block; margin: auto;" /> --- ### Predictive power grows with network size and disclosure <img src="figures/preds.png" width="1100" style="display: block; margin: auto;" /> <center> Performance of predictor of personal information of non-users grows with: network size (t), disclosure tendencies of users (ρ), and their product (t∗ρ) --- layout: true <div class="my-footer"><span> <a href=https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-018-0130-3> Collective aspects of privacy in the Twitter social network. David Garcia, Mansi Goel, Amod Agrawal, Ponnurangam Kumaraguru. EPJ Data Science (2018)</a></span></div> --- ## Study 2: Location in Twitter .pull-left[ <img src="figures/TwitterLocation.png" width="1100" style="display: block; margin: auto;" /> ] .pull-right[ - Random sample of 1,017 users - excl. bots, mass media - Ego network of reply links - 68,447 alters - Timelines: 157M tweets - Location from profile text + Google Maps API - Tweet metadata to identify users sharing contact lists ] --- ## Twitter shadow profile test <img src="figures/TwitterProblem.png" width="1100" style="display: block; margin: auto;" /> - Data represents a bidirectional social network - Joined date of users shows network growth - Tweet metadata contains posting app: disclosing users are only those who post at least once from Twitter mobile apps (shared contact lists) - Predicting location of users who didn't join yet based on disclosing friends already on Twitter --- ## Location prediction and heterogeneity <img src="figures/alters.png" width="1000" style="display: block; margin: auto;" /> - Location prediction of non-users greatly outperforms null model - Individuals with more alters sharing contact lists in Twitter have lower error --- ## Shadow profiles after leaving a social network <img src="figures/Shadow2.png" width="800" style="display: block; margin: auto;" /> <div style="font-size:15pt"> [**Privacy beyond the individual.** David Garcia. Nature Human Behavior (2019)](https://www.nature.com/articles/s41562-018-0513-2) [**Information flow reveals prediction limits in online social activity.** James Bagrow, Xipei Liu & Lewis Mitchell. Nature Human Behavior (2019)](https://doi.org/10.1038/s41562-018-0510-5) --- ### Complex privacy: Online privacy as a collective phenomenon .pull-left[ - The decision of individuals to share data is mediated by the decisions of others - Privacy externalities of the contract between a user and a platform - Complex problems, collective solutions: International data cooperatives ] .pull-right[ <img src="figures/wired.png" width="500" style="display: block; margin: auto;" /> ] <div style="font-size:16pt"> [**Online privacy as a collective phenomenon.** E. Sarigol, D. Garcia, F. Schweitzer. Second ACM Conference on Online Social Networks (COSN) (2014)](https://dl.acm.org/doi/10.1145/2660460.2660470) [**Leaking privacy and shadow profiles in online social networks.** D. Garcia, Science Advances, 3 (8) e1701172 (2017)](https://advances.sciencemag.org/content/3/8/e1701172.full) [**Collective Aspects of Privacy in the Twitter Social Network.** D. Garcia, M. Goel, A. Agrawal, P. Kumaraguru, EPJ Data Science, 7(3) (2018)](https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-018-0130-3) [**Privacy beyond the individual.** D. Garcia. Nature Human Behavior (2019)](https://www.nature.com/articles/s41562-018-0513-2)