PPT-Design and Implementation of Privacy‑Preserving

Author : olivia-moreira | Published Date : 2018-01-06

Surveillance Aaron Segal Yale University May 11 2016 Advisor Joan Feigenbaum an unspeakable blasphemy Dymaxion Overview Introduction Surveillance and Privacy

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Design and Implementation of Privacy‑Preserving: Transcript


Surveillance Aaron Segal Yale University May 11 2016 Advisor Joan Feigenbaum an unspeakable blasphemy Dymaxion Overview Introduction Surveillance and Privacy. Privacy by Design advances the view that the future of privacy cannot be assured solely by compliance with regulatory frameworks rather privacy assurance must ideally become an organizations default mode of operation Initially deploying PrivacyEnhan Z. . Zhong. , L. . Ramaswamy. and K. Li, IEEE, INFOCOM 2008. Intelligent E-Commerce System Lab.. Aettie. , . Ji. OUTLINE. INTORDUCTION. PRIOR WORK. THE ALPACAS ANTI-SPAM FRAMEWORK. Feature-Preserving Fingerprint. Database Services in the Cloud. Divy. . Agrawal. , . Amr. El . Abbadi. , . Shiyuan. Wang. University of California, Santa Barbara. {. agrawal. , . amr. , . sywang. }@cs.ucsb.edu. ICDE’2013 Tutorial. By: Moreno-Sanchez et al from Saarland University. Presented By: Cody Watson. Some Slides Borrowed From NDSS’15 Conference. What is a Credit . Network?. Weighted directed graph used to represent credit. Surveillance. Aaron . Segal. Yale University. May 11, 2016. Advisor: Joan . Feigenbaum. . “…an unspeakable blasphemy.” - @. Dymaxion. Overview. Introduction – Surveillance and Privacy. B. Hoh, M. . Gruteser. , H. . Xiong. , and A. . Alrabady. . ACM CCS. Presented by . Solomon Njorombe. Abstract. Motivation. Probe-vehicle automotive monitoring systems. Guaranteed anonymity in location traces datasets. . and. . Fault-Tolerant. Data. . Storage. Haibin Zhang (UConn). haibin. .zhang. @. uconn.edu. http://. scl.uconn.edu. /people/. haibin. /. info.php. Research. . Interests:. . Security. . and. . Prepared by: Eng. . Hiba. Ramadan. Supervised by: . Dr. . Rakan. . Razouk. . Outline. Introduction. key directions in the field of privacy-preserving data mining. Privacy-Preserving Data Publishing. Aaron Johnson. Vitaly Shmatikov. Background. Main goal: . discovering genetic basis for disease. Requires analyzing large volumes of genetic information from multiple individuals. Voluntary and mandated sharing of genetic datasets between hospitals, biomedical research orgs, other data holders. Why worry about your privacy?. Specific safety threats to military members and families. Identity theft for adults and children following data breaches. Desire to stay out of the media spotlight. A personal right in the US, guaranteed by the Constitution. Presented By:. Humam. . Nameer. 1. CASE STUDY. Just over three years old, . Clash of Clans.  rakes in more than $. 5M.  each day and consistently charts in the top three grossing . apps. .  It’s the app that new game developers look to for . Data Aggregation at Scale. Michael J. Freedman. Princeton University. Joint work with: Benny . Applebaum. , Haakon . Ringberg. , . Matthew Caesar, and Jennifer Rexford. Problem:. Network Anomaly Detection. Case Studies from the Data Linkage Program. Lisa B. Mirel. Board of Scientific Counselors. May 19, 2021. Introduction. Linking data is a powerful mechanism to provide policy relevant information in an efficient way. A. Konstantinidis. 1. , G. Chatzimilioudis. 1. , D. . . Zeinalipour-Yazti. 1. , . Paschalis Mpeis. 2. , . Nikos Pelekis. 3. , . Yannis. Theodoridis. 3. University of Cyprus. 1. . University of Edinburgh.

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