PPT-Differentially Private Data Analysis of Social Networks via Restricted Sensitivity

Jeremiah Blocki Avrim Blum Anupam Datta Or Sheffet Theory Lunch Fall 2012 Goal useful statistics Preserve Privacy and Release Useful Statistics 2 Outline Background Social Networks Differential Privacy. of Computer Science BenGurion University of the Negev kobbicsbguacil Sofya Raskhodnikova Dept of Computer Science and Engineering Pennsylvania State University sofyacsepsuedu Adam Smith Dept of Computer Science and Engineering Pennsylvania State Uni Priyanka. . Agrawal. Abstract. Social Networks to keep in touch with friends, family and community. Newer Web 2.0 technologies encourage Social Networking. Security and privacy, market and technological factors to be considered while developing social networks.. Computer Science & Engineering. . Pennsylvania State University. New Tools for Privacy-Preserving Statistical Analysis . IBM Research . Almaden. February 23, 2015. Privacy in Statistical Databases. Week 13. How is date being used. Predict Presidential Election - Nate Silver . – . http://adage.com/article/campaign-trail/nate-silver-s-election-predictions-a-win-big-data-york-times/238182. /. Predict Pregnancy - Target . Sonia Jahid. Department of Computer Science. University of Illinois at Urbana-Champaign. March 10, . 2011. www.soniajahid.com. 2. Statistics. Privacy Issues. Research on Online Social Network security and privacy. Week 13. How is date being used. Predict Presidential Election - Nate Silver . – . http://adage.com/article/campaign-trail/nate-silver-s-election-predictions-a-win-big-data-york-times/238182. /. Predict Pregnancy - Target . Node Differential . Privacy . Sofya. . Raskhodnikova. Penn State University. Joint work with. . . Shiva . Kasiviswanathan. . (. GE Research. ),. . Kobbi. . Nissim. . (. Ben-Gurion U. and Harvard U.. General Tools for Post-Selection Inference. Aaron Roth. What do we want to protect against?. Over-fitting from fixed algorithmic procedures (easiest – might hope to analyze exactly). e.g. variable/parameter selection followed by model fitting. 2, 2014. 1. Required Reading. A firm foundation for private data analysis. . Dwork. , C. Communications of the ACM, 54(1), 86-95. . 2011.. Privacy by the Numbers: A New Approach to Safeguarding Data. Erica . seq. data. Many recent algorithms for calling differentially expressed genes:. edgeR. : . Empirical analysis of digital gene expression data in . R. http://. www.bioconductor.org. /packages/2.10/. Chapter 1. Increasing ambivalence. Women in workforce vs. children in day care. Divorce vs. unhappy marriage. Focus on the individual. Individualism. Utilitarian individualism. Expressive individualism. McCarty. University of Florida. Books. Social Network Analysis: A Handbook. by John Scott, London: Sage (2000). . Social . Network Analysis: Methods and Applications. . Stanley Wasserman and Katherine Faust. Cambridge: Cambridge University Press (1994). . Kenneth Frank, College of Education and Fisheries and Wildlife. Help from: Ann Krause, Ben Michael Pogodzinski, Bo Yan, Min Sun, I-Chen, Chong Min Kim. Cep 991B Fall 2018. To participate on zoom you will click on . This project proposes to study sensitivity analysis for guiding the evaluation of uncertainty of data in the visual analytics process. We aim to achieve:. Semi-automatic Extraction of Sensitivity Information.