Despite the critical role anonymous and private communication plays in the offline world, the state of user privacy in the online world is grim. Current Internet networking protocols provide no support for masking the identity of communication endpoints. An adversary with access to router nodes can monitor traffic patterns and harvest IP addresses. Tracking software, such as Carnivore/Echelon, can be used to map IP addresses back to individual users. While encryption schemes, like SSL, can make it computationally difficult for attackers to decipher what was sent, they cannot hide who sent it. The situation is particularly problematic when governments and corporations engage in online monitoring and censorship, as the current set of digital communication protocols enable user tracking at an unprecedented scale.

CliqueNet is a peer-to-peer, self-organizing, scalable communication protocol that guarantees anonymity. It has three critical properties:

  1. it unconditionally hides the identity of the source and destination of a packet, even from attackers with arbitrary wiretapping capabilities,
  2. it scales well with increasing numbers of hosts, and
  3. it is resilient against malicious and disruptive participants.
The central abstraction provided by CliqueNet is that of an anonymous communication channel that supports a completely anonymous broadcast operation, as well as a sender-anonymous, efficient unicast primitive. This anonymous dial tone is akin to an Ethernet carrier, and supports traditional internetworking protocols such as TCP. In short, CliqueNet is a practical, scalable, and robust protocol, which can serve as a modular communication substrate for peer-to-peer applications that require strong anonymity and privacy guarantees.

CliqueNet has evolved into Herbivore. Please see the Herbivore pages for the current status of the project.

->  CliqueNet FAQ Frequently Asked Questions about CliqueNet.
->  Related Work Pointers to other projects for anonymous communication.
->  Project Members Who we are.

Computer Science Department
Cornell University