Tag Archives: George Danezis

Cyber Security Summer School 2015

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13-17 July 2015, Laulasmaa Spa and Conference Hotel.

Topics:
* How to live securely in a digital society?
* E-Estonia, a role model for the future? On implementation, challenges and limitations
* Privacy and other concerns of a digital society
* Anonymisation and deanonymisation techniques
* Internet measurements and routing: big data and network mathematics
* Examples and hands-on activities from experts
* Lots of fun and insights into controversial topics

Tentative Program:
Sunday, July 12: 18.30 Welcome Reception

Monday, July 13:  Living in a digital society, securely?
09.30 – 13.00 Steven M. Bellovin
13.00 – 14.00 Lunch
14.00 – 17.30 Jaan Priisalu & Kristjan Vassil
18.30 Dinner

Tuesday, July 14: Privacy and concerns about a digital society
09.00 – 12.30 Ben Zevenbergen
12.30 – 13.30 Lunch
13.30 – 17.00 George Danezis
18.30 Dinner

Wednesday, July 15: Security Ecosystems
09.00 – 12.30 Vern Paxson
12.30 – 13.30 Lunch
13.30 – 17.00 Richard Kemmerer
18.30 Dinner
20.00 – 22.00 Mehis Hakkaja (Hacking Demo)

Thursday, July 16: Internet measurements and routing: big data and mobile networks
09.00 – 12.30 Tristan Henderson
12.30 – 13.30 Lunch
13.30 – 17.00 Walter Willinger
18.00 Transport to the gala dinner location
19.00 Gala dinner

Friday, July 17: Student presentations
09.00 – 12.30 Students presentations
12.30 – 13.30 Lunch
13.30 – 14.30 Students presentations
14.30 Closing remarks
15.00 Transport to Tallinn and departure

The registration to summer school has already ended.

Links:
http://studyitin.ee/c3s/program

PhD thesis: “Privacy-preserving statistical analysis using secure multi-party computation”

liina_kamm_PhD_thesis

Linna Kamm PhD thesis: “Privacy-preserving statistical analysis using secure multi-party computation”
Defense date: 09.03.2015 – 16:15 to 17:45 (J. Liivi 2-404, Tartu, Estonia)

Thesis supervisor: Senior Research Fellow Sven Laur

Opponents:
PhD Rebecca N. Wright Rutgers University (USA)
PhD George Danezis University College London

Summary:
This work focuses on how to perform statistical analyses in a way that preserves the privacy of the individual. To achieve this goal, we use secure multi-party computation. This cryptographic technique allows data to be analysed without seeing the individual values. Even though using secure multi-party computation is a time-consuming process, we show that it is feasible even for large-scale databases.

Links:
http://www.ut.ee/en/events/liina-kamm-privacy-preserving-statistical-analysis-using-secure-multi-party-computation