The smart grid is a revolutionary upgrade to the existing power grid. It uses smart devices that have communication and computation capabilities to use electricity more efficiently, reduces CO2 emissions, and integrates renewable energy resources. One of the main components of the smart grid is the advanced metering infrastructure (AMI) networks that can connect smart meters installed at consumer side to the utility. In AMI networks, smart meters should send fine-grained power consumption data to the utility for management, state estimates ... etc. This data can reveal sensitive information about the consumers' activities like the appliances they are currently using, when they leave/return home ... etc. Various techniques that extensively use asymmetric key cryptography operations have been widely proposed in order to enable the utility to collect the power consumption data while preserving the consumer's privacy. However, these techniques typically involve large overhead in terms of computation and communication. Furthermore, most of the existing schemes are vulnerable to collusion attacks and the existing approaches address privacy preservation without addressing data integrity. Attackers can modify smart meter's readings to report wrong data to the utility.;In this thesis, we propose an efficient secure and privacy-preserving scheme that utilizes efficient symmetric key cryptography and hashing operations to collect power consumption data. The idea is based on sending masked power consumption readings with message authentication code (MAC) from the smart meters to the utility and removing these masks by adding all the smart meters' messages, so that the utility can learn the aggregated reading, but cannot learn the individual readings to preserve privacy. The meter masks its reading with the summations of all the masks shared with the other meters (called proxies) and the mask can be removed when adding the proxies' messages because each proxy adds the shared mask value to its readings. To extract a meter's readings, all the proxies in the network (including the gateway and the utility) should collude, but to extract the aggregated measurements all the meters messages should be added together. Also, by using the MACs on the homomorphic hashes of the messages, the utility can check the integrity of the aggregated reading received from the meters without accessing the individual reading to preserve privacy. We also introduce a key management procedure that uses asymmetric key operations, but unlike the power consumption collection that is done frequently, the key management procedure is run over a very long time for calculating the seed key. The seed key is then used to generate session keys that are used for data encryption and computing shared masks. Our measurements demonstrate that the cryptographic operations needed in our scheme are much more efficient than the operations needed in the existing schemes. In addition, our analysis demonstrates that the proposed scheme can preserve the consumers' privacy and protect against collusion attacks. The analysis also demonstrates that our data integrity and key management techniques are secure against known attacks. Finally, ns-3 simulation results demonstrate that the network performance of the proposed scheme outperforms the performance of the existing schemes due to a reduced packet size and computational overhead.
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