1. What’s the scuttlebutt on Secure Gossip? Robbert van Renesse

2. Secure Gossip or Gossip for Security? Secure gossip: making sure probabilistic guarantees hold in the face of malicious behavior Gossip for security: exploit robustness of gossip for spreading important information (certificate revocation lists, security patches, …) movement from former to latter?

3. Scale  Security Gossip often said to scale well But in large settings more chance of bad behavior of participants –Spreading false rumors –Introducing too many rumors –Not forwarding good rumors –…

4. What have people worked on? Integrity –Preventing spreading of false gossip without the use of digital signatures Availability –Making sure everybody receives all gossip Fairness –Everybody does its share of forwarding

5. 8 years - 8 papers 1999 Malkhi, Mansour, Reiter –On diffusing updates in a Byzantine environment 2001 Malkhi, Reiter, Sella –Optimal unconditional information diffusion 2003 Minsky, Schneider –Tolerating malicious gossip 2003 Malkhi, Pavlov, Sella –Gossip with malicious parties 2004 Badishi, Keidar, Sasson –Exposing and eliminating vulnerabilites to DoS attacks in secure gossip- based multicast 2006 Johansen, Allavena, Van Renesse –Fireflies: Scalable Support for Intrusion-Tolerant Network Overlays 2006 Haridasan, Van Renesse –Defense Against Intrusion in a Live Streaming Multicast System 2006 Li, Clement, Wong, Napper, Roy, Alvisi, Dahlin –BAR Gossip

6. Malkhi et al. take 1 (Srikanth/Toueg) Integrity without digital signatures –Assume initially at least t +1 nodes have valid gossip –A node does not forward gossip until it thinks it is valid –A gossip becomes valid when it has been received from at least t + 1 nodes Bad nodes cannot generate valid gossip Slow…

7. Malkhi et al. take 2 –Initially t + 1 nodes have valid gossip –Nodes maintain set of source paths for each gossip. Initially 1 empty path. –Gossip messages contain all paths. –On receipt of gossip from p, a node adds p to all paths before adding to set of paths. –On receipt of t + 1 disjoint paths, gossip can be delivered. Fast, but very large messages

8. Malkhi et al. take 3 Minsky & Schneider Practical versions that only maintain a subset of paths Useful up to a dozen bad nodes or so But there’s always public key signatures…

9. Keidar et al. Prevent application-level DoS attacks –Send digests on publicly known ports, actual data on randomly generated ports –Randomly pick messages to exchange from digests to prevent being overwhelmed by bogus messages

10. Van Renesse, Allavena, Johansen FOO gossip (Fireflies Ordered Rings) Large fraction of bad nodes –Generate a pseudo-random mesh that connects all correct nodes with high probability –Gossip on mesh, using public key crypto for integrity –Use persistent connections between nodes, reducing need for full merges –Throttle state transfer for joins Moderate scalability, but can be fixed I think

11. Haridasan, Van Renesse SecureStream Video streaming with many bad hosts –Carefully flood video frames on pseudo- random mesh using a notify/pull strategy –Prefer nodes that sent data to you over nodes that don’t Deals pretty well with freeloaders Approach under development deals well with heterogeneity as well

12. Li, Alvisi et al. BAR gossip Elegant game-theoretic approach to dealing with rational and Byzantine nodes (See RFC3092 for FOOBAR origins)

13. Fireflies Gossip Sign data for integrity Even if large fraction of participants do not forward gossip, still O(log N) completion time But Byzantine members can launch a “reverse” Denial-of-Service attack –By claiming to know nothing –Causes correct members to xmit all state –Root cause: lack of connections

14. Solution: partial membership gossip with persistent connections Erdös & Rényi: if probability of link is high enough, graph will be connected with high probability Chung & Lu: such a graph will have logarithmic diameter

15. Pseudo-Random Mesh Each member obtains public key certificate from CA Member’s id is verified by CA Each member calculates k positions using a secure hash function –H ( id, ring )  position Connect to successor and predecessor k rings: k chosen such that correct members form a connected graph with high probability

16. Working out the math… k : out degree N : correct + Byzantine members n : correct members  : probability of graph being connected But what about crashed members???

17. Fireflies Membership Members are correct, crashed, or Byzantine (Cannot necessarily tell Byzantine from correct or crashed members) Correct members have a view  p, q both correct  q in p’s view  p correct, q crashed  q not in p’s view With probabilistic real-time bounds…

18. Protocol Structure MembershipGossip PingingSet Reconciliation Deliver within  Accuse and rebut Monitor and suspect Exchange diffs EACH COMPONENT IS PROBABILISTIC

19. Here’s the problem… If correct members can make mistakes and falsely accuse correct members, how can we prevent Byzantine members from falsely accusing correct members??? (Answer: we don’t have to)

20. Membership Each member can only accuse its 2t + 1 successors 2t + 1 rings: t chosen such that each member has no more than t Byzantine predecessors whp

21. Data Structures NoteAccusation 12435 53 Node identifier 12435 53 Node identifier Ring: 3 Accuser: identifier Both signed by creator Both gossiped  everybody’s got the same set t + 1 rings enabled t rings disabled Serial numbe r

22. Membership Protocol 1.On each ring, monitor the first successor that hasn’t been accused 2.Gossip accusation if successor suspect 3.If you’re accused, gossip a new note (rebuttal) with a new serial number And disable the corresponding ring! 4.Remove from view those members who’ve been accused for longer than 2   is the gossip dissemination time

23. PlanetLab evaluation setup Configuration –t = 12 (25 monitoring rings) –Gossip rate = 1 gossip / 3.5 seconds Byzantine members: –10% aggressive + 10% passive –aggressive attacks: accuse at any opportunity do not forward rebuttals –passive attacks: never accuse do not forward accusations

24. Protocol overhead on PlanetLab # members bytes/sec

25. What’s still needed? Mandatory Information Flow –Gossip on need-to-know basis –Eliminate covert channels Fair Sharing (Congestion Control) –Prevent some from using up all capacity Secure Aggregation –Much like electronic voting –Everybody counted exactly once More?

26. Let’s look at use cases first.. Important timely disseminations that are undesired by some and likely to be attacked –Certificate Revocation Lists –Security patches –Political messages Needs availability & integrity

27. Use cases, cont’d Video Streaming on the cheap –Webcasting –tele-conferencing –Entertainment Needs availability & mandatory information flow & fairness & fair sharing

28. Use cases, cont’d Monitoring & Auditing –PlanetLab –Cooperative Web Caching –… Needs secure aggregation & flow control

29. Use cases, cont’d Replication –Files –Control data Needs availability & integrity & information flow

30. Valid Accusations Accusation is valid iff –Accusation is correctly signed by accuser, and –Ring is enabled by the accused, and –Accuser is a monitor of the accused: Accuser is direct predecessor on a ring, or holds valid accusations for everybody in between accuser and accused Recursive relation (without base case) complicates correctness proof…

31. Fireflies Group Membership Group membership protocol –Tolerant of Byzantine failures –Probabilistic… –Provides and uses secure gossip infrastructure Fireflies have on/off behavior and use mimicry to dupe and devour related species…

32. Assumptions Network can lose messages But correct members form a connected communication graph of fair links Byzantine members can collude But cannot break crypto, and DDoS attacks can be detected and suppressed Bounded P byzantine

33. Informal Insight Gossip implements something like an atomic broadcast (whp) –Gets around problems associated with asynchrony, such as FLP result –Atomic broadcast can implement consensus, which is approximately what we need here…

34. How to pick t ? Large enough so that the probability of having more than t Byzantine predecessors is smaller than   = 1/N  E(problem) = 1

35. “max” propagation time (  )

36. PlanetLab Deployed on >150 PlanetLab nodes Disclaimer: PlanetLab results are not repeatable and biased