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Secure Data Aggregation in Wireless Sensor Networks: A Survey. Yingpeng Sang, Hong Shen Yasushi Inoguchi, Yasuo Tan, Naixue Xiong Proceedings of the Seventh International Conference on Parallel and Distributed Computing,Applications and Technologies (PDCAT'06) Presented by kevin wang. Preview.
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Secure Data Aggregation in Wireless Sensor Networks: A Survey Yingpeng Sang, Hong Shen Yasushi Inoguchi, Yasuo Tan, Naixue Xiong Proceedings of the Seventh International Conference on Parallel and Distributed Computing,Applications and Technologies (PDCAT'06) Presented by kevin wang
Preview • Main contributions • Outline • Classify by infrastructure in WSNs • Classify by encryption in WSNs • Proposed two general schemes • Hop by hop • End to end • Conclusions
Main contributions • Past • Only focus on data confidentiality or data integrity • Now • Survey the work • Hop-by-hop • End-to-end • Propose security frameworks respectively for • Hop-by-hop • End-to-end • Both on Data confidentiality and Data integrity
What is confidentiality • Confidentiality • Ensuring that information is accessible only to those authorized to access • One of the cornerstones of Information security • The delivering data is confidential in WSNs • For avoiding to leak secret information, the sensed data have to encrypt to keep confidentiality Sensor or aggregator sink node M Enk(M) Dnk(M)=M
What is integrity • Integrity • Ensuring that only authorized parties are able to modify computer system assets and transmitted information • One of the cornerstones of Information security • The delivering data is sensitive in WSNs • For avoiding to modify the secret information, the sensed data have to keep integrity • Especially, in a cheaper and simple device
Outline in this paper • A survey paper for data aggregation in WSN • Proposed two data aggregation scheme for HBH and ETE respectively
Problem definition • How to satisfy the confidentiality and integrity in WSN
Server Server Header Sensor Nodes Sensor Nodes Sacrificed Node Classify with Infrestructure • Wireless sensor networks • HWSN • Hierarchical Wireless Sensor Networks • DWSN • Distributed Wireless Sensor Networks
Classify with Data aggregation • Hop-by-hop • Adv: deliver package size small • Disadv: key management • Pair wise key dist. DWSN • Group wise key dist. HWSN • perform operators: sum, min, max, avg, count, median…
Classify with Data aggregation • End-to-end • Adv: the secrets share between sink and sensor • Disadv: much redundant are sent • Can not perform above operators • The sensed data have been encrypted
Server Header Sensor Nodes Sacrificed Node Background-network model-HWSN R S A F A
Server Sensor Nodes Background-network model-DWSN R s S
Background-security requirements • Confidentiality • Eavesdropping • Compromised node’s key • Using the compromised node’s keys to deduce all secret information in entire network • Using the compromised key to inject unauthorized malicious nodes in network. • Integrity • Injecting arbitrary chosen malicious data into the compromised S. • Modifying, forging, or discarding messages in the compromised A and F.
Background-aggregation functions • Sum • Average • Median • Minimum • Maximum • Count
Hop-by-hop encrypted data aggregation in WSN • 1.Security bootstrapping • 1.1Pair-wise key distribution DWSN (confidentiality) • Master key based solution [14] • All nodes use one key • Pair-wise key pre-distribution solution • Each node shares one key with sink • Random key pre-distribution solution [10] [7] • Using key ring to find one common key • Key pre-distribution schemes with deployment knowledge [15][10] • DDHV’s scheme • Other solution [5][9][16]
Hop-by-hop encrypted data aggregation in WSN • 1.Security bootstrapping • 1.2Group-wise key distribution HWSN (confidentiality) • Symmetric group-wise key distribution [2],1992 • A symmetric key can be generate among t nodes • Asymmetric group-wise key distribution [18], 2004 • ECC • EC-public/private
Hop-by-hop encrypted data aggregation in WSN • 2.Data integrity • Some related work assume that confidentiality is protected by pre-deployed key. • [12], L. Hu and D. Evans, “Secure aggregation for wireless networks”, In Workshop on Security and Assurance in Ad hoc Networks, Jan 2003. • [18], A. Mahimkar, T. S. Rappaport, “SecureDAV: A Secure Data Aggregation and Verification Protocol for Sensor Networks”, Proceedings of IEEE GlobalTelecommunications Conference (Globecom) 2004,Nov, 2004, Dallas, TX, USA. • [21], B. Przydatek, D. Song, and A. Perrig, “SIA: Secure Information Aggregation in Sensor Networks”,In Proc. of ACM SenSys 2003, 2003.
Sum(Aggr) MAC(KASi,Aggr) KASi Secure aggregation for wireless networks, 2003 • Node A, deployment, symmetric pair-wise key, KAS, RA=reading data from node A • Data transmission phase • KASi=E(KAS, i) • Parent node B and aggregated result =Aggr • MAC(KASi,Aggr) • Data validation phase • R will verifies the final aggregated results using the pair-wise keys • Lower communication cost • Vulnerable • Nodes, aggregators, forwarding nodes are easy to be compromised
Sum(Aggr) MAC(KASi,Aggr) KASi SecureDAV: A Secure Data Aggregation and Verification Protocol for Sensor Networks, 2004 • Using Merkle Hash Tree to improve [12] • Data transmission phase • A: MAC (KASi=E(KAS, i), RA) • Parent node B and aggregated result =Aggr, generate a hash value of RA by Merkle Hash function: H(RA) • Aggregator sends MAC (Aggr, H(RA, i)) to sink node, R • Data validation phase • R will verifies the final aggregated results using the pair-wise keys and queries the aggregators what hash values did they sent • The queries is to check individual readings • Vulnerable • high communication cost
SIA: Secure Information Aggregation in Sensor Networks,2003 • It can engage an interactive proof with the aggregator and check whether the aggregator result is correct. • Key point • Their correct build on the related trust Sum(Aggr) MAC(KASi,Aggr) KASi
Consequence • Communication cost • [21]<[18]<[12]
End-to-end data aggregation in WSN • Network-wise key distribution • Master key based solutions, 2005, CEG[6], 2005, CDA[11] • Public key based solution, 2006[19] • Data integrity • Compared to HBH, there is no efficient scheme to protect integrity in ETE • In [23], 2004, each node sends its reading to R using ETE, • The R employs truncation and trimming on the RA’s to achieve robust aggregation result against spoofed sensor.
Proposed two frameworks for data aggregation in WSN-HBH • Framework 1: Hop-by-hop encrypted data aggregation • 1.The bootstrapping phase • For controlled environment HWSN, • group-wise key can be generated for all nodes within each cluster • For uncontrolled environment DWSN, • Pair-wise key can be distributed among each pair of sensor node • 2.The aggregator selection phase • R can select aggregators to construct a transmission structure with minimum energy cost
Proposed two frameworks for data aggregation in WSN • Framework 1: Hop-by-hop encrypted data aggregation • 3.The data aggregation phase • EKai,A(xi)A:(DKai,A(xi)):sum then R • 4.The data transmission phasec • EKai,A(xi)+MHT(EKsi,R, (xi)) • 5.The data integrity verification phase • R hashes all (EKsi,R) to check again • Decrypt (EKsi,R) and aggregate to check correct?
Consequence • Framework 1. • Confidentiality • For HWSN group-wise key • For DWSN Pair-wise key • Integrity • Merkle Hash Tree
Proposed two frameworks for data aggregation in WSN-ETE • Framework 2: End-to-end encrypted data aggregation • 1.The bootstrapping phase and the aggregator selection phase • For HWSN and DWSN use network-wise public key K • 2.The data aggregation phase • Using ECC-ElGamal to aggregate and reach homomorphic encryption
Proposed two frameworks for data aggregation in WSN • Framework 2: End-to-end encrypted data aggregation • 3.The data transmission phase • Noses will commit all (EKsi,R,(xi)) of its children by MHT to R • 4.The data integrity verification phase • R check the commitment hash of all (EKsi,R,(K))
Consequence • Confidentiality • network-wise public key K • Integrity • Merkle Hash Tree
Security analysis • Compromised some nodes, R will detect with Merkle hash tree • Compromised some aggregators, R will detect with Merkle hash tree • Compromised some nodes and aggregators, R will not detect with Merkle hash tree • HBH more efficient than ETE • HBH less secure than ETE, in compromised some nodes.
Conclusions • Survey and classify the related work into HBH and ETE data aggregation scheme • Proposed two schemes for data aggregation in HBH and ETE, respectively.
Comment • Good • Know the data aggregation field • Research history • More • This schemes did not consider the no response nodes problem • Consider MST + dynamic routing path to reduce the end-to-end communication cost to increase entire lifetime • Past did not consider nodes will be exhausted, then have to change path in end to end environment.