Privacy Enhanced Resilient Data Aggregation in Cluster Based Wireless Sensor Network
저자
발행사항
고양 : 한국항공대학교대학원, 2009
학위논문사항
Thesis(doctoral)-- 한국항공대학교대학원 : 컴퓨터공학과 2009. 8
발행연도
2009
작성언어
영어
발행국(도시)
대한민국
형태사항
; 26cm
소장기관
The proliferation of sensor networks provides a promising solution for avariety of ubiquitous data services, but it is facing the data security and privacy challenges because of its unfavorable deployment nature of being prone to physical attacks with circumscribed source available. Considering the scalability and energy consumption, the hierarchical cluster-based topology is commonly accepted as an optimal structure for sensor network, which can increase communication scalability, prolong network lifetime, and reduce data redundancy. However, the data privacy and security are still kept unsolved in Cluster-based Wireless Sensor Network (CBWSN) due to its highly constrained resources and opening deployments, which make it infeasible to directly apply traditional cryptography and therefore vulnerable to various attacks.
As security in the traditional network, attack detection, attack prevention and attack tolerance are three aspects for security in CBWSN. The focus of my research is on attack prevention and tolerance in terms of privacy and resiliency, not covering the attack detection in this dissertation. According to the intention of attackers against data aggregation, the attacks can be classified as three categories: eavesdropping, data disruption and hijacking. By
eavesdropping, like cracking the message or directly capture the node, the hacker try to extract the transferred messages and possible key information; the hacker can disrupt sensing data and further render the aggregation result
meaningless by node compromise or manipulating environment, the hacker also can disrupt the aggregation by compromising the cluster head; the hacker even can take over the control of the whole network by compromised trust
nodes, which is hijacking. This dissertation primarily studies the prevention and resilience strategies against eavesdropping and data disruption attacks, considering energy consumption, secure data transmission and resilient data aggregation. For achieving these three goals, we employed additive homomorphism encryption before data aggregation to save energy consumption; we proposed a new encryption algorithm to subgroup based density mining for data aggregation which can tolerate node compromise to some extend and data disruption as well; we executed secure scalar product to enhance privacy while using density based clustering for multi-dimension data aggregation.
First, a novel additive homomorphic encryption algorithm is designed to achieve the target of privacy preserving data fusion. Since hop-by-hop encryption is too expensive for sensor network and also vulnerable to various kinds of attacks and normal end-to-end encryption cannot support efficient data fusion, a secure and efficient solution for data aggregation in CBWSN is essential. The proposed homomorphic encryption can provide addition aggregation directly on the ciphertext without decryption, which can save amount of energy consumption. This scheme could be a perfect solution if there were no node compromise attacks. Because the scheme works well on the condition
that all the sensing nodes use the same encryption key. In addition, the additive aggregation function makes the scheme very violated to the aggregation result even under one malicious value.
Second, an advanced scheme is considered to face severe problems of the compromise attacks and data disruption attacks discussed in the above scheme. Subgroup based key distribution and density based clustering are introduced
in our dissertation as the second step to tolerant some compromise attacks. We proposed a new encryption algorithm to support privacy enhanced resilient data aggregation. The encryption algorithm has the characteristic that encrypted data from same group can be compared without decryption. Based on this encryption algorithm, The proposed scheme can not provide density based clustering between subgroups but within each subgroup, which provides secure aggregation for each group using the representative node in stead of simple adding up all the value, which can also endure node compromise to some extent because of the dierent keys used in different subgroups, furthermore, which can provide data distribution estimation on the BS according to the collected aggregation results. For evaluating the aggregation performance,
we define three performance matrix such as aggregation eciency, aggregation accuracy and data distribution recovery. We discuss the system performance using these matrixes under these attacks by case study. The experiment results show that this scheme can give reasonable aggregation results even under 50% malicious readings, much more robust than sum, average, max or min functions which are commonly used in the aggregation schemes. However, with the dimension increasing, the communication cost of this scheme is quite high, so we found a new solution for high-dimension data aggregation.
Third, a resilient data fusion on multi-dimension data is further discussed by employing secure scalar product and density based data mining on concealed data. This scheme is proposed because of the higher communication cost of the second scheme on multi-dimension data. We use RC5/RC6 for data encryption and secure scalar product to get the corresponding data similarity without knowing the exact sensing value. The extracted similarity can be fed to density based data mining to elect representative data as
aggregation result. This scheme doesn't have any limits on the encryption key, every node can use a unique key because the data encryption and data mining are separated, but their results are fed together to aid the secure and resilient aggregation. Since the density based clustering for aggregation, this scheme has similar performance against data disruption as the second scheme, but with better tolerance against node compromise because of the unique key on each sensor node. One thing interesting to mention here is this scheme can not work on 1-dimension data aggregation. Because this scheme could work securely only if the dimension of the vector is not less than 2 times of the size of the random vector. For 1-dimension data aggregation, the random vector should have less than 1/2 elements, that means less than 1 vector. If we don't use the random vector, the cluster head can easily know the original data, which cannot support the privacy preserving target, even for cluster head to know the original data value.
In summary, this dissertation gives a comprehensive study of the data aggregation methodologies in CBWSN from data privacy, data reliability and data efficiency. We systematically and comprehensively study and proposed
privacy-enhanced data aggregation schemes on 1-dimension data and multi-dimension data respectively with proposed new encryption algorithm. We also did simulation and give performance evaluation about these schemes under different ratio of attacks in the network. The experiment results show that the integration of data mining and cryptography may provide a more reliable and robust aggregation solution for wireless sensor network, which cost a little of additional overheads in memory and communication.
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