End-to-end Secure Data Aggregation in Wireless Sensor Networks
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1 End-to-end Secure Data Aggregation in Wireless Sensor Networks Keyur Parmar 1 Devesh Jinwala 2 1 Ph.D Scholar & Senior Research Fellow Department of Computer Engineering SVNIT, Surat, India 2 Professor Department of Computer Engineering SVNIT, Surat, India December 18, 2014 Keyur Parmar [email protected] The Tenth ICISS /34
2 Outline 1 Introduction 2 Preliminaries 3 MR-CDA 4 Conclusions Keyur Parmar [email protected] The Tenth ICISS /34
3 Outline 1 Introduction 2 Preliminaries 3 MR-CDA 4 Conclusions Keyur Parmar [email protected] The Tenth ICISS /34
4 Introduction Wireless Sensor Nodes Equipped with Sensing and Actuation Capability Self-organizing Self-healing Highly Constrained Resources Processor Bandwidth Memory and Storage Space Communication and Computation Power Sensor Node = Sensing + Processing + Communication Akyildiz et al. Wireless Sensor Networks: A Survey. Computer Networks, Elsevier, 38(4), , (2002). Keyur Parmar [email protected] The Tenth ICISS /34
5 In-network Processing (Data Aggregation) Reduces Data Transmission Improves Energy Efficiency Fasolo et al. In-network Aggregation Techniques for Wireless Sensor Networks: A Survey. Wireless Communications, IEEE 14(2), 70-87, (2007). Keyur Parmar [email protected] The Tenth ICISS /34
6 Secure Data Aggregation Types of Secure Data Aggregation Hop-by-Hop Secure Data Aggregation End-to-End Secure Data Aggregation (CDA) Ozdemir et al. Secure Data Aggregation in Wireless Sensor Networks: A Comprehensive Overview. Computer Networks, 53(12), , (2009). Keyur Parmar [email protected] The Tenth ICISS /34
7 Concealed Data Aggregation Security Requirements Integrity/Message Authentication Confidentiality/Privacy Replay Protection Keyur Parmar The Tenth ICISS /34
8 Outline 1 Introduction 2 Preliminaries 3 MR-CDA 4 Conclusions Keyur Parmar [email protected] The Tenth ICISS /34
9 Privacy Homomorphism Homomorphic Primitives Homomorphic Encryption Homomorphic Message Authentication Codes Rivest et al. On Data Banks and Privacy Homomorphisms. Foundations of Secure Computation 4(11), , (1978) Agrawal, S. et al. Homomorphic MACs: MAC-based Integrity for Network Coding. In: Proceedings of the 7th ACNS, pp Springer, Berlin, Heidelberg, (2009). Keyur Parmar The Tenth ICISS /34
10 Privacy Homomorphism - Example Castelluccia et al. Efficient and provably secure aggregation of encrypted data in wireless sensor networks. ACM Transactions on Sensor Networks, 5(3), 20:1-20:36, Keyur Parmar [email protected] The Tenth ICISS /34
11 Privacy Homomorphism - Example Keyur Parmar [email protected] The Tenth ICISS /34
12 Privacy Homomorphism - Example Keyur Parmar [email protected] The Tenth ICISS /34
13 Privacy Homomorphism - Example Keyur Parmar [email protected] The Tenth ICISS /34
14 Privacy Homomorphism - Example Keyur Parmar [email protected] The Tenth ICISS /34
15 Privacy Homomorphism - Example Castelluccia et al. Efficient and provably secure aggregation of encrypted data in wireless sensor networks. ACM Transactions on Sensor Networks, 5(3), 20:1-20:36, Keyur Parmar [email protected] The Tenth ICISS /34
16 Privacy Homomorphism - Tradeoffs Malleability Non-Malleability Security Level Non-Malleability Adaptive Chosen Ciphertext Attack Malleability Chosen Ciphertext attack Keyur Parmar [email protected] The Tenth ICISS /34
17 Outline 1 Introduction 2 Preliminaries 3 MR-CDA 4 Conclusions Keyur Parmar [email protected] The Tenth ICISS /34
18 MR-CDA - Example Node 1 computes, C 1 = E BS (M 1 ) T 1 = MAC(C 1 ) E 1,3 (T 1 ) E 1,5 (T 1 ) Node 1 transmits, 1 3 C 1 E 1,3 (T 1 ) E 1,5 (T 1 ) Keyur Parmar [email protected] The Tenth ICISS /34
19 MR-CDA - Example Node 2 computes, C 2 = E BS (M 2 ) T 2 = MAC(C 2 ) E 2,3 (T 2 ) E 2,5 (T 2 ) Node 2 transmits, 2 3 C 2 E 2,3 (T 2 ) E 2,5 (T 2 ) Keyur Parmar [email protected] The Tenth ICISS /34
20 MR-CDA - Example 1 Node 3 decrypts, T 1 = D 1,3 (T 1 ) 2 Node 3 generates, T 1 = MAC(C 1) 3 Node 3 compares, T 1 = T 1? 4 Node 3 decrypts, T 2 = D 2,3 (T 2 ) 5 Node 3 generates, T 2 = MAC(C 2) 6 Node 3 compares, T 2 = T 2? Keyur Parmar [email protected] The Tenth ICISS /34
21 MR-CDA - Example Node 3 computes, C 3 = C 1 C 2 T 3 = T 1 T 2 E 3,5 (T 3 ) E 3,7 (T 3 ) Node 3 transmits, 3 5 C 3 E 3,5 (T 3 ) E 3,7 (T 3 ) E 1,5 (T 1 ) E 2,5 (T 2 ) Keyur Parmar [email protected] The Tenth ICISS /34
22 MR-CDA - Example Node 5 receives, 3 5 C 3 E 3,5 (T 3 ) E 3,7 (T 3 ) E 1,5 (T 1 ) E 2,5 (T 2 ) Node 5 receives, 4 5 C 4 E 4,5 (T 4 ) E 4,7 (T 4 ) Keyur Parmar [email protected] The Tenth ICISS /34
23 MR-CDA - Example 1 Node 5 decrypts, T 1 = D 1,5 (T 1 ) T 2 = D 2,5 (T 2 ) T 3 = D 3,5 (T 3 ) T 4 = D 4,5 (T 4 ) 2 Node 5 generates, T T 3 = MAC(C 3) 4 = MAC(C 4) 3 Node 5 compares, T 3 = T 3? T 4 = T 4? 4 Node 5 compares, T 3 = T 1 T 2 Keyur Parmar [email protected] The Tenth ICISS /34
24 MR-CDA - Example Node 5 computes, C 5 = C 3 C 4 T 5 = T 3 T 4 E 5,7 (T 5 ) E 5,8 (T 5 ) Node 5 transmits, 5 7 C 5 E 5,7 (T 5 ) E 5,8 (T 5 ) E 3,7 (T 3 ) E 4,7 (T 4 ) Keyur Parmar [email protected] The Tenth ICISS /34
25 MR-CDA - Example Node 7 receives, 5 7 C 5 E 5,7 (T 5 ) E 5,8 (T 5 ) E 3,7 (T 3 ) E 4,7 (T 4 ) Node 7 receives, 6 7 C 6 E 6,7 (T 6 ) E 6,8 (T 6 ) Keyur Parmar [email protected] The Tenth ICISS /34
26 MR-CDA - Example 1 Node 7 decrypts, T 3 = D 3,7 (T 3 ) T 4 = D 4,7 (T 4 ) T 5 = D 5,7 (T 5 ) T 6 = D 6,7 (T 6 ) 2 Node 7 generates, T T 5 = MAC(C 5) 6 = MAC(C 6) 3 Node 5 compares, T 5 = T 5? T 6 = T 6? 4 Node 7 compares, T 5 = T 3 T 4 Keyur Parmar [email protected] The Tenth ICISS /34
27 MR-CDA - Example Node 7 computes, C 7 = C 5 C 6 T 7 = T 5 T 6 E 7,8 (T 7 ) Node 7 transmits, 7 8 C 7 E 5,8 (T 5 ) E 6,8 (T 6 ) E 7,8 (T 7 ) Keyur Parmar [email protected] The Tenth ICISS /34
28 MR-CDA - Example Node 8 receives, 7 8 C 7 E 5,8 (T 5 ) E 6,8 (T 6 ) E 7,8 (T 7 ) Keyur Parmar [email protected] The Tenth ICISS /34
29 MR-CDA - Example 1 Node 8 decrypts, T 5 = D 5,8 (T 5 ) T 6 = D 6,8 (T 6 ) T 7 = D 7,8 (T 7 ) 2 Node 8 generates, T 7 = MAC(C 7) 3 Node 8 compares, T 7 = T 7? 4 Node 8 compares, T 7 = T 5 T 6 5 Node 8 decrypts, D BS (C 7 ) = M 1 M 2 M 4 M 6 Keyur Parmar [email protected] The Tenth ICISS /34
30 MR-CDA - Example D 7,8 (T 7 ) = MAC(C 7 )? 1 : 0 D 5,8 (T 5 ) D 6,8 (T 6 ) = D 7,8 (T 7 )? 1 : 0 8 D 5,7 (T 5 ) = MAC(C 5 )? 1 : 0 D 6,7 (T 4 ) = MAC(C 6 )? 1 : 0 D 3,7 (T 3 ) D 4,7 (T 4 ) = D 5,7 (T 5 )? 1 : 0 C 7 =C 5 C 6 & T 7 =T 5 T 6 C 7, E 7,8 (T 7 ), E 5,8 (T 5 ), E 6,8 (T 6 ) D 3,5 (T 3 ) = MAC(C 3 )? 1 : 0 D 4,5 (T 4 ) = MAC(C 4 )? 1 : 0 D 1,5 (T 1 ) D 2,5 (T 2 ) = D 3,5 (T 3 )? 1 : 0 C 5 =C 3 C 4 & T 5 =T 3 T 4 C 5, E 5,7 (T 5 ), E 5,8 (T 5 ), E 3,7 (T 3 ), E 4,7 (T 4 ) C 6, E 6,7 (T 6 ), E 6,8 (T 6 ) D 1,3 (T 1 ) = MAC(C 1 )? 1 : 0 D 2,3 (T 2 ) = MAC(C 2 )? 1 : 0 C 3 =C 1 C 2 & T 3 =T 1 T 2 C 3, E 3,5 (T 3 ), E 3,7 (T 3 ), E 1,5 (T 1 ), E 2,5 (T 2 ) 3 4 C 4, E 4,5 (T 4 ), E 4,7 (T 4 ) C 1, E 1,3 (T 1 ), E 1,5 (T 1 ) 1 2 C 2, E 2,3 (T 2 ), E 2,5 (T 2 ) Keyur Parmar [email protected] The Tenth ICISS /34
31 Outline 1 Introduction 2 Preliminaries 3 MR-CDA 4 Conclusions Keyur Parmar [email protected] The Tenth ICISS /34
32 Conclusions Data Aggregation and Privacy Data aggregation - Plaintext data Privacy - Encrypted data Keyur Parmar [email protected] The Tenth ICISS /34
33 Conclusions Data Aggregation and Privacy Data aggregation - Plaintext data Privacy - Encrypted data Data Aggregation and Message Authentication Data aggregation modifies the original data Message authentication requires the original data Keyur Parmar [email protected] The Tenth ICISS /34
34 Conclusions Data Aggregation and Privacy Data aggregation - Plaintext data Privacy - Encrypted data Data Aggregation and Message Authentication Data aggregation modifies the original data Message authentication requires the original data Privacy and Message Authentication Privacy - Encrypted data Authentication - Plaintext data Keyur Parmar [email protected] The Tenth ICISS /34
35 Publication/Acknowledgement Publication Keyur Parmar and Devesh C. Jinwala, Malleability Resilient Concealed Data Aggregation, in Proceedings of the 20th EUNICE/IFIP WG 6.2, 6.6 Workshop on Advances in Communication Networking, France, ser. EUNICE 14. Lecture Notes in Computer Science (LNCS), pp , Springer-Verlag, Berlin, Heidelberg, Keyur Parmar The Tenth ICISS /34
36 Publication/Acknowledgement Publication Keyur Parmar and Devesh C. Jinwala, Malleability Resilient Concealed Data Aggregation, in Proceedings of the 20th EUNICE/IFIP WG 6.2, 6.6 Workshop on Advances in Communication Networking, France, ser. EUNICE 14. Lecture Notes in Computer Science (LNCS), pp , Springer-Verlag, Berlin, Heidelberg, Acknowledgement This research is a part of the project A Secure Data Aggregation System and An Intrusion Detection System for Wireless Sensor Networks. It is supported by the Department of Electronics and Information Technology, Ministry of Communications and Information Technology, Government of India. Keyur Parmar [email protected] The Tenth ICISS /34
37 Get your facts first, then you can distort them as you please. Mark Twain Thank You... Keyur Parmar The Tenth ICISS /34
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