re made up of trusted users that are not categorised as SPITters. An end-user accepts the calls, or messages, initiated by any of the members in his/her white list. On the contrary, black lists contain any/all potential initiators of SPIT calls. These calls are therefore to be blocked. Content filtering: This method is based on filters that scan the content of messages. They appear to be inappropriate as anti-SPIT, since real-time filtering is hard to accomplish. Nevertheless, this technique could be used for the detection of instance messaging SPIT (similar to e-mail spam). Challenge-response. Communication becomes possible only once the caller has replied correctly to a challenge sent by the callee. This approach aims at preventing SPIT by operating a distinction between humans and 'bots'. Other such mechanisms include Turing tests and computational puzzles. Consent-based. Here, communication is not achieved unless the callee explic- itly consents. Reputation-based. Trust is the central notion of this approach. When a callee receives a request for communication the level of trust of the caller should be determined. This is accomplished through direct estimations or second-hand reputations. If the trust level is above a predefined threshold then the communication is permitted, otherwise it is rejected. SIP addresses management: One of the SPITters' main goals is to collect as many valid addresses as possible. So, to prevent SPIT it is important for end-users to protect their addresses (i.e., URIs) from being collected. For this to be done, two different approaches are possible: address obfuscation and use of multiple addresses. Charging-based. This approach obliges SPITters to pay for every unsolicited bulk call (messages), as a result, the cost born by the SPITter is increased in terms of financial or computational resources. As more attention is drawn to the SPIT problem, a number of anti-SPIT frameworks begin to emerge. They are commented upon briefly in the sequel. 19.5.1.1 SPIT Prevention Using Anonymous Ve