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ISSN: 2278 – 1323 International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 3, May 2012 II. NETWORK CODING MODEL LEV(2,1) What is Network coding? Random 𝑥1 + 2𝑥2 + 2𝑥3 ∗ 2 122 combination + 3𝑥1 + 𝑥2 + 2𝑥3 ∗ 1 𝒚𝟏 𝒇 𝟏 (𝒚 𝟏, 𝒚 𝟐, 𝒚 𝟑 ) 2 = (5𝑥1 + 5𝑥2 + 6𝑥3 ) 2 𝒚𝟐 Router Input Output Buffer Buffer 𝒚𝟑 𝒇 𝟐 𝒚 𝟏, 𝒚 𝟐, 𝒚 𝟑 312 2 Sending 2Fig. 1 Network coding: network nodes can compute functions 𝑥1 + 2𝑥2 + 2𝑥3 566 of input messages. 3𝑥1 + 𝑥2 + 2𝑥3 (Global encoding Vector) Consider a router in a computer network. Today, a router Fig. 2 Random network Codingcan merely route, or forward messages. Each message onan output link must be a copy of a message that derived Additivity: Given the cipher text (x) and (y), there exists aearlier on an output link Network coding, in contrast, allows computationally efficient algorithm Add(. , .) such thateach node in a network to perform some computation. E(X+Y) = Add( E(x), E(y)).Therefore in network coding, each message sent on a node’s Scalar Multiplicity: Given E(x) and a scalar t, there existsoutput link ca be some “function” or” mixture” of messages a computationally efficient algorithm Mul(. , .) such thatthat arrived earlier on the input’s link, as illustrated in Fig 1. E(x. t) = Mul(E(x), t).Thus, network coding is generally the transmission, mixing Benaloh [10] and Paillier[11] cryptosystems are of such an(or encoding), and re-mixing (or re-encoding) of messages additive HEF, where the addition on plaintext can bearriving at nodes inside the network, such that the transmitted achieved by performing a multiplicative operation on themessages can be unmixed (or decoded) at their final corresponding cipher text, i.e, E(x1 + x2) = E(x1) . E(x2).destinations. Network coding was first introduced by Ahlswede et al IV THE PROPOSED PRIVACY PRESERVATION[12] as shown in Fig.1. Subsequently, two key techniques, TECHNIQUErandom coding [13] and linear coding [14] further promoted The proposed technique consists of three phases: Sourcethe development of network coding. The random codin encoding, intermediate node recoding and sink decoding.makes network coding more practical, while the linear coding Without loss of generality, we assume that each sink acquiresis proven to be sufficient and computationally efficient for two keys, the encryption key ek and decryption key dk, fromnetwork coding . currently, network coding has been widely an offline Trust Authority (TA). For supporting multicast, arecognized as a promising information dissemination group of sinks are required to obtain from the TA. Then, theapproach to improve network performance. encryption key is published and the decryption key is kept Unlike other packet-forwarding systems, network coding secret.allows intermediate nodes to perform computation on Source Encoding: Block diagram of source encoding phaseincoming messages, making outgoing messages be the as shown in Fig.6. Consider that a source has h messages, saymixture of incoming ones. This elegant principle implies a X1, . , . , . ,Xh, to be sent out. The source first prefixes h unitrandom network coding [13], as shown in Fig. 2, where there vectors to the h messages, respectively. After tagging asis a transmission opportunity for an outgoing link, an shown in Fig.5, the source can choose a random LEV andoutgoing packet is formed by taking a random combination perform linear encoding on these messages, respectively.of packets in the current buffer. An overview of networkcoding and possible applications has been given in [15]. In Then a LEV can produce an encoded message with GEV’spractical network coding, source information should be are explicit. So any node can recover the original messagesdivided into blocks with h packets in each block. All coded hence we are using homomorphic encryption functions topackets related to the kth block belong to generation k and encrypt the GEV.random coding is performed only among the packets in the To offer confidentiality for the tags, homomorphicsame generation. Packets within a generation need to be encryption operations are applied as follows:synchronized by buffering for the purpose of network codingat intermediate nodes. 𝐶𝑖 𝑒 = 𝐸 𝑒𝑘 𝑔𝑖 𝑒 , 1≤ 𝑖≤ℎ (1) 𝐶 𝑒 = [𝐶1 𝑒 , 𝐶2 𝑒 , … , 𝑐ℎ 𝑒 ] III HOMOMORPHIC ENCRYPTION FUNCTION Intermediate Recoding: Block diagram of this phase is asHomomorphic Encryption Functions (HEFs) have the shown in Fig.7, After receiving a number of packets of theproperty of homomorphism, which means operations on same generation, an intermediate node can perform randomplaintext can be performed by operating cipher text as shown linear coding on these packets. To generate an outgoingin Fig. 3.If E(.) is a HEF, E(x+y) can be plaintext x and y. To packet, firstly, a random LEV[β1, . . . βh] is chosenbe applicable in the corresponding plain text scheme, a HEF independently; then a linear combination of message content( . ) needs to satisfy the following properties. 178
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ISSN: 2278 – 1323 International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 3, May 2012 Random network coding Encrypted Encoded New Encrypted Encoded GEV Message GEV Message Fig 7 Intermediate Node Recoding Phase Decryption with dk Decoding Encrypted Encoded Decrypted Encoded Original GEV Message GEV Message Message Fig 8 Sink Decoding Phase V RESULT VI CONCUSIONThe message encryption window is as shown in Fig.9, which In this paper we have proposed an efficient network codingis used to encrypt the message which we are sending from based privacy preservation technique against traffic analysissource to destination by using homomorphic encryption and flow tracing in multi hop wireless networks. And we canfunction with encryption key ek. First the message will split detect how many attackers are trying to attack to hack theinto number of paths after that message will encrypt by using message. With homomorphic encryption on global encodingencryption key ek. Without knowing the encryption key vectors (GEVs), the proposed technique offers twoattackers cannot get the original message. If an attacker tries significant privacy preserving features packet flowto obtain the message with guessed decryption key than they untracability and message content confidentiality, withwill get some other message. When we click simulate with efficiently thwart traffic analysis/flow tracing attacks. Withrouting button the traffic analysis window as shown in Fig.10 homomorphic encryption, the proposed technique keeps thewill appear. essence of random linear network coding and each sink can discover the source message by inverting GEVs with veryThe traffic analysis window is used to prevent traffic probability.analysis/flow tracing by detecting number of attackers which Network performance of the proposed technique is betterare trying to hack the messages. First it will create the path than the previous schemes because of intrinsic feature; lessfrom source to destination after creating path it will start computation cost because packets from the same session cantransmission of packets. When we click the make intrusion be encoded together; maximize the throughput and consumesbutton it will show which nodes are members and which are less energy.not members of path and also detect the number of attackerswith their target node. Fig. 9 Message Encryption window Fig. 10 Traffic Analysis Window 180
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