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Effective and secure content retrieval in unstructured p2p… Since it is the unstructured P2P network, there is no central control. In order to provide security to the user, we preferred the self certification technique. It provides a unique identity to each and every peer in the network. That identity is known as the self certificate. It consists of information such as serial number, public key, IP address, port address. These certificates are assigned for the trust of the user. After that by using the content auditing method, the user can able to identify whether the received content is hacked or not. If the contents are matched with the training set, then the user will receive the original content. If the contents are not matched with the training set, then the user will receive the hacked (modified) content. Then the user can also predict the rate of the content auditing. Figure.3.Signature gets verified during content searching, if there is no hacked content Figure.4.Signature doesn‟t gets verified and identified hack Figure.5. Content Auditing Rating after identify the hack www.<strong>ijcer</strong>online.com ||May ||2013|| Page 113
Effective and secure content retrieval in unstructured p2p… VI. CONCLUSION By examining the results of the bloom hash table, we found that this is significantly faster than a normal hash table using the same amount of memory, hence it can support better throughput for router applications that use hash tables. We here propose an efficient and secured full-text retrieval scheme in an unstructured P2P networks using Bloom Cast method. Bloom Cast method guarantees the recall with high probability. Thus it is considered more effective. The overall communication cost of a full-text search is reduced below a formal bound. Thus it is one among the reason that Bloom Cast is efficient and effective among other schemes. Moreover the communication cost for replication is also reduced because we replicate Bloom Filters instead of the raw documents across the network. During the transfer of files there is possible of distribution of viruses, worms and trojan horses and malicious peers to overcome this the self certification (MD5 with RSA) is used, it provides authentication and authorization. It easily finds the malicious peers and aborts the transaction. Therefore the proposed method provides the efficient and secure communication between the peers. Further the content auditing method helps the user to find the rate of the hacked (modified) content by fixing the probability level using the training data sets that are pre-defined by the user. REFERENCES [1] R.A. Ferreira, M.K. Ramanathan, A. Awan, A. Grama, and S. Jagannathan, “Search with Probabilistic Guarantees in Unstructured Peer-to-Peer Networks,” Proc. IEEE Fifth Int‟l Conf. Peer to Peer Computing (P2P ‟05), pp. 165-172, 2005. [2] E. Cohen and S. Shenker, “Replication Strategies in Unstructured Peer-to-Peer Networks,” Proc. ACM SIGCOMM ‟02. pp. 177-190, 2002. [3] P. Reynolds and A. Vahdat, “Efficient Peer-to-Peer Keyword Searching,” Proc. ACM/IFIP/USENIX 2003 Int‟l Conf. Middleware (Middleware ‟03), pp. 21-40, 2003. [4] H. Song, S. Dharmapurikar, J. Turner, and J. Lockwood, “Fast Hash Table Lookup Using Extended Bloom Filter: An Aid to Network Processing,” Proc. ACM SIGCOMM, 2005. [5] C. Tang and S. Dwarkadas, “Hybrid Global-Local Indexing for Effcient Peer-to-Peer Information Retrieval,” Proc. First Conf. Symp. Networked Systems Design and Implementation (NSDI ‟04),p. 16, 2004. [6] D. Li, J. Cao, X. Lu, and K. Chen, “Efficient Range Query Processing in Peer-to-Peer Systems,” IEEE Trans. Knowledge and Data Eng., vol. 21, no. 1, pp. 78-91, Jan. 2008. [7] G.S. Manku, “Routing Networks for Distributed Hash Tables,” Proc. ACM 22nd Ann. Symp. Principles of Distributed Computing (PODC ‟03), pp. 133-142, 2003. [8] H. Shen, Y. Shu, and B. Yu, “Efficient Semantic-Based Content Search in P2P Network,” IEEE Trans. Knowledge and Data Eng.,vol. 16, no. 7, pp. 813-826, July 2004. [9] A. Broder and M. Mitzenmacher, “Network Applications of Bloom Filters: A Survey,” Internet Math., vol. 1, no. 4, pp. 485-509, 2004. [10] V. King and J. Saia, “Choosing a Random Peer,” Proc. ACM 23 rd Ann. Symp. Principles of Distributed Computing (PODC ‟04), pp. 125-130, 2004. [11] I. Stoica, R. Morris, D. Karger, M.F. Kaashoek, and H.Balakrishnan, “Chord: A Scalable Peer-to-Peer Lookup Service for Internet Applications,” Proc. ACM SIGCOMM ‟01, pp. 149-160, 2001. [12] C. Tang and S. Dwarkadas, “Hybrid Global-Local Indexing for Effcient Peer-to-Peer Information Retrieval,” Proc. First Conf. Symp. Networked Systems Design and Implementation (NSDI ‟04), p. 16, 2004. [13] F.M. Cuenca-Acuna, C. Peery, R.P. Martin, and T.D. Nguyen, “Planetp: Using Gossiping to Build Content Addressable Peer-to-Peer Information Sharing Communities,” Proc. 12th IEEE Int‟l Symp. High Performance Distributed Computing (HPDC ‟03), pp. 236-246, 2003. [14] K. Sripanidkulchai, B. Maggs, and H. Zhang, “Efficient Content Location Using Interest-Based Locality in Peer-to- Peer Systems,”Proc. IEEE INFOCOM ‟03, 2003. [15] M. Li, W.-C. Lee, A. Sivasubramaniam, and J. Zhao, “SSW: A Small-World-Based Overlay for Peer-to-Peer Search,” IEEE Trans. Parallel and Distributed Systems, vol. 19, no. 6, pp. 735-749, June 2008. www.<strong>ijcer</strong>online.com ||May ||2013|| Page 114
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