Identity attack and anonymity protection for P2P-VoD systems

Abstract

Abstract-As P2P multimedia streaming service is becoming more popular, it is important for P2P-VoD content providers to protect their servers identity. In this paper, we first show that it is possible to launch an "identity attack": exposing and identifying servers of peer-to-peer video-on-demand (P2P-VoD) systems. The conventional wisdom of the P2P-VoD providers is that identity attack is very difficult because peers cannot distinguish between regular peers and servers in the P2P streaming process. We are the first to show that it is otherwise, and present an efficient and systematic methodology to perform P2P-VoD servers detection. Furthermore, we present an analytical framework to quantify the probability that an endhost is indeed a P2P-VoD server. In the second part of this paper, we present a novel architecture that can hide the identity and provide anonymity protection for servers in P2P-VoD systems. To quantify the protective capability of this architecture, we use the "fundamental matrix theory" to show the high complexity of discovering all protective nodes so as to disrupt the P2P-VoD service. We not only validate the model via extensive simulation, but also implement this protective architecture on PlanetLab and carry out measurements to reveal its robustness against identity attack.

Security Engineering of Patient-Centered Health Care Information Systems in Peer-to-Peer Environments: Systematic Review

“…Content pollution is the most popular and common attack in P2P streaming systems [ 74 ]; it was detected in 50%-80% of files in KaZaA and about 50% of popular files in eDonkey [ 73 , 74 ]. Pollution is not necessarily caused by malicious users; P2P systems are notorious for illegally sharing and disseminating copyrighted content, and content is often polluted by copyright owners as a countermeasure to protect their rights when legal actions fail [ 71 , 72 ]. To facilitate the protection of copyright claims, some P2P system providers even weaken protection from pollution attacks in their network [ 73 ], although this affects the confidence of users in such systems [ 72 , 73 ].…”

Distributed mitigation of content pollution in peer‐to‐peer video streaming networks

“…We also plan to investigate dynamic and self-adaptive monitoring intervals which could be increased or decreased in runtime based on the system load. presents a model and estimates content pollution propagation [11][12][13] ✓ employs band codes to construct black lists of polluters [47] ✓ network coding is employed to allow the identification of polluters and limit content pollution [32] ✓ proposes the application of black lists to live streaming [17,27] ✓ transmits hash-based signatures of all chunks; the challenge is to receive hashes in advance for live streaming [21,24] ✓ applies cryptography to every whole chunk and employs a distributed key management scheme [36] ✓ employs peer groups to ensure chunk integrity; the server publishes content information in that group; peers access the group to verify chunks integrity [34] ✓ Merkle-trees are employed that use hashes to guarantee the integrity of streams of chunks [35] presents an evaluation of black lists, cryptography, hash-based verification and digital signatures [38] presents an evaluation of the impact of pollution attacks; shows that the impact depends on the stability of the network, and on the bandwidth available at both malicious peers and the source [37] evaluates content authentication mechanisms to live streaming [39,40] ✓ employs reputation and ranking to file sharing P2P systems [22] ✓ employs reputation and ranking to live streaming; the reputation mechanisms are based on peer experience [23] shows that reputation-based approaches can suffer with the collusion of malicious peers, with false positives, and present a high delay to propagate conclusions [41] a confidence management strategy is proposed based on retransmissions of the polluted data; depending on the situation the number of retransmissions can be high [42] in order to prevent DDoS attacks on streming sources proposes a strategy to hide source identity; used in the context of P2P VoD [43] presents a through evaluation of SopCast reaches the conclusion that a single attacker can harm up to 50% of peers and consume up to 30% of the available bandwidth [44] a centralised (non-distributed) solution is proposed to detect polluters in live streaming networks employing comparasionbased system-level diagnosis [this work] ✓ presents a distributed strategy that employs comparison-based diagnosis to combat pollution in live streaming; each peer independently identifies and stops requesting chunks from its polluter neighbours…”

“…In [9] a strategy is proposed based on an overlay to monitor and isolate content pollution in video-on-demand (VoD) P2P networks. Earlier, in [42] the authors presented a strategy to hide source identity in these networks. The purpose is to avoid DDoS attacks directed at the sources, which can completely destroy live streaming sessions.…”

“…This is different from our strategy in the sense that we simply compare chunks as they are retransmitted by neighbours and avoid polluters. Another work also based on coding [42] requires chunks to be modified with an extra field that has to be processed by all peers for all chunks. In our strategy there is no need to modify the chunks, furthermore in our case only a percentage of chunks is selected for comparisons.…”

Diagnosis of Content Pollution in P2P Live Streaming Networks

“…In [20] the authors present a strategy to hide the identity of source servers in P2P-VoD (Video-on-Demand) networks. This is important as with that information a bad intentioned entity can direct attacks such as DDoS to those servers in order to harm the live streaming session.…”