Anonymity on the Internet Presented by Randy Unger

Types of Anonymity Pseudonymity – Susceptible to subpoenas Sender – Receiver / observer can’t identify sender Receiver – Observer can’t identify receiver Sender-receiver – Observer can’t identify that communication has been sent

Uses of Anonymity Positive Free speech for political claims as well as non-political comments engage in whistle-blowing conduct commercial transactions freedom from detection, retribution, and embarrassment New York Times Co. vs. Sullivan, 1964 "an author's decision to remain anonymous...is an aspect of the freedom of speech protected by the First Amendment"

Uses of Anonymity Negative Spam DoS - Illegal activity – anonymous bribery, copyright infringement, harassment, financial scams, disclosure of trade secrets

Assumptions Weak attacker – Eavesdrops on first and last hop – Can introduce messages here Strong attacker – Eavesdrops on all links – Can introduce messages anywhere Attacker has finite time, computing power Multiple users

Types of Attackers Local eavesdropper – Observes inbound and outbound messages on user’s computer Administrator – Operator or group of operators of anonymizing systems attempting to foil their own system Remote attack – Observation at the remote end by eavesdropper or attack by the remote host

Attacks Timing Attack, Volume Attack – Watches shape of traffic instead of content Flooding Attack – With batch size n, attacker sends n-1 messages Usage Pattern Attack – Consistent usage patterns leads to predictability

Levels of Anonymity Absolute Privacy Beyond Suspicion Probable Innocence Possible Innocence ExposedProvably Exposed Beyond Suspicion Attacker can see evidence of a sent message, but the sender appears no more likely to be the originator than any other potential sender in the system Probable Innocence The sender is more likely the originator than any other potential sender, but there is equal likelihood the sender is not the originator Possible Innocence The sender appears more likely to be the originator than to not be the originator, but there’s still a non-trivial probability that the originator is someone else

Capabilities Latency, Bandwidth, Anonymity – Pick 2 Human element – Repetitive usage patterns make attacks easier – Pizza effect

Proxy Anonymizers Use trusted centralized servers Anonymous r ers - Helsingius Anonymizer.com Hides IP address - NAT Users not anonymous to proxy server Susceptible to traffic analysis

Mixes Source routing chosen by user Shuffles order of packets Mix cascade consists of several mixes under separate operators Encrypted for each mix in the path Processes packets in batches Used to counter traffic analysis

Mixes A1, C1(A3, C3(A2, C2(S, M, r2), r3), r1) A3, C3(A2, C2(S, M, r2), r3) A2, C2(S, M, r2) S, M Mix 1 Mix 4 Mix 3 Mix Ai = Next Hop Address Ci = Message encrypted with public key of Mix i S = Destination Host address M = Original message

Mixes Fine for non real-time ( ) Not sufficient for VoIP, video, web Mix waits to accumulate inputs to process as a batch (especially slow for low traffic)

Enhancements Messages all the same length Buffers messages until several can be sent at once Dummy messages inserted – Between mixes – Between mixes and user Balance end to end throughput with anonymity – Duration to wait for mixes to accumulate traffic – Percentage of dummy traffic

P5P5 Decentralized – Harder to attack Allows choice of tradeoff between anonymity / throughput Encrypted with public key of each node in route Nodes change packet order Fixed message size Users have broadcast map and route map Noise packets counter statistical traffic analysis

User A User B User A can send an anonymous message to User B via group */0, 1/1, 111/3, etc User A can route messages between 00/2 and 01/2 Broadcast hierarchy independent of network topology 01/2 is a subset of */0 – more efficient but less anonymous Hash of User’s public key provides choice of groups.

P5P5 Within a channel, P 5 functions as a mix cascade Between channels, P 5 provides greater anonymity per bandwidth – For 8192 users, 1.5 Mbps provides 200Kbps with 40% loss Resistant to Timing/Volume and DoS attacks Susceptible to Flood Attack (Mob Attack) – User’s channel is flooded, prompting him to reveal more of his mask to gain efficiency, thereby reducing his anonymity

Conclusion Costly to be anonymous – Tradeoff with throughput Can not be completely anonymous anyway – No protection from monitoring usage patterns Aside from this, practical anonymity can be achieved