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Efficient aggregation of encrypted data in Wireless Sensor Network

This paper, presented by Yi Cheng Lin on March 13, 2007, explores innovative methods for securing data in Wireless Sensor Networks (WSNs) through efficient aggregation techniques. By combining additive homomorphic encryption with streamlined data aggregation methods, the authors Einar Mykletun and Gene Tsudik demonstrate a system that conserves battery power and enhances data security. The study compares traditional hop-by-hop aggregation with the proposed scheme, outlining the trade-offs between bandwidth efficiency and security. The results indicate that while the new method may use slightly more bandwidth, it offers significantly improved protection for sensitive data.

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Efficient aggregation of encrypted data in Wireless Sensor Network

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  1. Efficient aggregation of encrypted data in Wireless Sensor Network Author: Einar Mykletun, Gene Tsudik Presented by Yi Cheng Lin Date: March 13, 2007

  2. Outline • Introduction • Conditions • Additive Homomorphic Encryption • Aggregation • Result • Conclusion

  3. Introduction • Security in Wireless sensor networks (WSNs) is a popular research topic in recent years • Aggregation techniques are used to reduce the amount of data communicated within a WSN and thus conserves battery power • This paper blend inexpensive encryption techniques with simple aggregation methods to achieve very efficient aggregation of encryption data

  4. Conditions(1/2) • Multi-level network tree (3-ary) • Additive homomorphic encryption M: message space C: ciphertext space M is a group under operation C is a group under operation c1 = Enck1(m1), c2 = Enck2(m2)

  5. Conditions(2/2) • Computing the average and variance • The packet header is 56 bits • End-to-end aggregation in WSNs • Compare to hop-by-hop (HBH) and No-Agg

  6. Additive Homomorphic Encryption

  7. Additive Homomorphic Encryption • n different ciphers ci • M >= >= with t = max(mi) • The key stream k can be generated by using a stream cipher, such as RC4 t

  8. Aggregation • Computing the Average • cxi = Enc(xi, ki, M),M = n*t, log(M)=log(n)+log(t) • Cx = • Sx = Dec(Cx, K, M) = Cx – K (mod M), where K = • Avg = Sx/n • Computing the Variance • yi= xi2, cyi= Enc(yi, ki’, M’),M’ = n*t2 ,log(M’)=2*log(n)+log(t) • Cy = • Vx = Dec(Cy, K’, M) = Cy – K’ (mod M),where K’ = • Variance = Vx/n – Avg2

  9. Result

  10. Conclusion(1/2) • This scheme is slightly less bandwidth efficient than the hop-by-hop aggregation scheme • However it provides a much stronger level of security • One limitation of this proposal is that the identities of the on-responding nodes need to be sent along with the aggregate to the sink

  11. Conclusion(2/2) • Reduce n (number of nodes) or t (value)

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