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CSE 592 INTERNET CENSORSHIP (FALL 2015) LECTURE 19 PHILLIPA GILL - STONY BROOK U.

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Presentation on theme: "CSE 592 INTERNET CENSORSHIP (FALL 2015) LECTURE 19 PHILLIPA GILL - STONY BROOK U."— Presentation transcript:

1 CSE 592 INTERNET CENSORSHIP (FALL 2015) LECTURE 19 PHILLIPA GILL - STONY BROOK U.

2 WHERE WE ARE Last time: Mitigating timing attacks (Astoria) Today: Finish up mitigating timing attacks (LASTor) Other approaches to anonymity systems; Dissent Aqua Administravia: Mark update on Piazza.

3 THE DISSENT PROJECT Goal: rethink the foundations of anonymity Offer quantifiable and measurable anonymity Build on primitives offering provable security Don't just patch specific vulnerabilities, butrearchitect to address whole attack classes http://dedis.cs.yale.edu/dissent/ Not a drop-in replacement for onion routing, but offers some systematic defense against all 5 classes of vulnerabilities ACKs: http://dedis.cs.yale.edu/dissent/pres/131024-austin.pdfhttp://dedis.cs.yale.edu/dissent/pres/131024-austin.pdf

4 ACKs: http://dedis.cs.yale.edu/dissent/pres/131024-austin.pdfhttp://dedis.cs.yale.edu/dissent/pres/131024-austin.pdf

5 ACKs: http://dedis.cs.yale.edu/dissent/pres/131024-austin.pdfhttp://dedis.cs.yale.edu/dissent/pres/131024-austin.pdf

6 ACKs: http://dedis.cs.yale.edu/dissent/pres/131024-austin.pdfhttp://dedis.cs.yale.edu/dissent/pres/131024-austin.pdf

7 DINING CRYPTOGRAPHERS (DC-NETS) 3 cryptographers eating dinner and the waiter informs them that the meal has been paid by someone Cryptographers want to know if it was one of them or the NSA They respect each others right to make an anonymous payment … … but want to know if the NSA paid Solution: 2 stage protocol 1.Each pair of cryptographers exchanges a secret (e.g., flip a coin behind a menu) 2.Announce a bit; XOR of bits shared with neighbors (if they did not pay) or the opposite of this (if they did pay)

8 EXAMPLE OF DINING CRYPTOGRAPHERS

9 ACKs: http://dedis.cs.yale.edu/dissent/pres/131024-austin.pdfhttp://dedis.cs.yale.edu/dissent/pres/131024-austin.pdf

10

11

12 ACKs: http://dedis.cs.yale.edu/dissent/pres/131024-austin.pdfhttp://dedis.cs.yale.edu/dissent/pres/131024-austin.pdf

13 ACKs: http://dedis.cs.yale.edu/dissent/pres/131024-austin.pdfhttp://dedis.cs.yale.edu/dissent/pres/131024-austin.pdf

14 ACKs: http://dedis.cs.yale.edu/dissent/pres/131024-austin.pdfhttp://dedis.cs.yale.edu/dissent/pres/131024-austin.pdf

15 ACKs: http://dedis.cs.yale.edu/dissent/pres/131024-austin.pdfhttp://dedis.cs.yale.edu/dissent/pres/131024-austin.pdf

16 ACKs: http://dedis.cs.yale.edu/dissent/pres/131024-austin.pdfhttp://dedis.cs.yale.edu/dissent/pres/131024-austin.pdf

17 ACKs: http://dedis.cs.yale.edu/dissent/pres/131024-austin.pdfhttp://dedis.cs.yale.edu/dissent/pres/131024-austin.pdf

18 ACKs: http://dedis.cs.yale.edu/dissent/pres/131024-austin.pdfhttp://dedis.cs.yale.edu/dissent/pres/131024-austin.pdf

19 ACKs: http://dedis.cs.yale.edu/dissent/pres/131024-austin.pdfhttp://dedis.cs.yale.edu/dissent/pres/131024-austin.pdf

20 ACKs: http://dedis.cs.yale.edu/dissent/pres/131024-austin.pdfhttp://dedis.cs.yale.edu/dissent/pres/131024-austin.pdf

21 ACKs: http://dedis.cs.yale.edu/dissent/pres/131024-austin.pdfhttp://dedis.cs.yale.edu/dissent/pres/131024-austin.pdf

22 ACKs: http://dedis.cs.yale.edu/dissent/pres/131024-austin.pdfhttp://dedis.cs.yale.edu/dissent/pres/131024-austin.pdf

23 ACKs: http://dedis.cs.yale.edu/dissent/pres/131024-austin.pdfhttp://dedis.cs.yale.edu/dissent/pres/131024-austin.pdf

24 ACKs: http://dedis.cs.yale.edu/dissent/pres/131024-austin.pdfhttp://dedis.cs.yale.edu/dissent/pres/131024-austin.pdf

25 ACKs: http://dedis.cs.yale.edu/dissent/pres/131024-austin.pdfhttp://dedis.cs.yale.edu/dissent/pres/131024-austin.pdf

26 ACKs: http://dedis.cs.yale.edu/dissent/pres/131024-austin.pdfhttp://dedis.cs.yale.edu/dissent/pres/131024-austin.pdf

27 ACKs: http://dedis.cs.yale.edu/dissent/pres/131024-austin.pdfhttp://dedis.cs.yale.edu/dissent/pres/131024-austin.pdf

28 TOWARDS EFFICIENT TRAFFIC- ANALYSIS RESISTANT ANONYMITY NETWORKS Stevens Le Blond David Choffnes Wenxuan Zhou Peter Druschel Hitesh Ballani Paul Francis

29 29 Snowden wants to communicate with Greenwald without Alexander to find out Ed’s IP Glenn’s IP

30 THE PROBLEM OF IP ANONYMITY Client Server 30 VPN proxy Proxies are single point of attack (rogue admin, break in, legal, etc)

31 31 Proxy Traffic analysis Onion routing (Tor) Onion routing doesn’t resist traffic analysis (well known)

32 OUTLINE 32

33 ANONYMOUS QUANTA (AQUA) k-anonymity: Indistinguishable among k clients BitTorrent Appropriate latency and bandwidth Many concurrent and correlated flows 33

34 34 Threat model Global passive (traffic analysis) attack Active attack Edge mixes aren’t compromised

35 Padding 35 Constant rate (strawman) Defeats traffic analysis, but overhead proportional to peak link payload rate on fully connected network

36 OUTLINE 36

37 37 Multipath Multipath reduces the peak link payload rate Padding

38 VARIABLE UNIFORM RATE 38 Reduces overhead by adapting to changes in aggregate payload traffic

39 OUTLINE 39

40 K-ANONYMITY SETS (KSETS) 40 Send ksetRecv kset Provide k-anonymity by ensuring correlated rate changes on at least k client links Padding

41 FORMING EFFICIENT KSETS 41 Epochs 1 2 3 Peers’ rates 1 2 3 Are there temporal and spatial correlations among BitTorrent flows?

42 OUTLINE 42

43 METHODOLOGY: TRACE DRIVEN SIMULATIONS Month-long BitTorrent trace with 100,000 users 20 million flow samples per day 200 million traceroute measurements Models of anonymity systems Constant-rate: Onion routing v2 Broadcast: P5, DC-Nets P2P: Tarzan Aqua 43

44 OVERHEAD @ EDGES 44 Models Overhead Much better bandwidth efficiency

45 THROTTLING @ EDGES 45 Models Throttling Efficiently leverages correlations in BitTorrent flows

46 OUTLINE 46

47 ONGOING WORK 47 Prototype implementation Aqua for VoIP traffic “tiny-latency” (RTT <330ms) Intersection attacks Workload independence

48 TAKE HOME MESSAGES Efficient traffic-analysis resistance by exploiting existing correlations in BitTorrent traffic At core: Multipath reduces peak payload rate Variable uniform rate adapts to changes in aggregate payload traffic At edges, ksets: Provide k-anonymity by sync rate on k client links Leverage temporal and spatial correlations of BitTorrent flows 48

49 HANDS ON ACTIVITY (Try at home ) Dissent source code is publicly available: https://github.com/DeDiS/Dissent Try downloading/installing/running the system 49


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