Credential-based privacy-preserving power request scheme for smart grid network

A smart grid network adjusts power allocation by collecting information about the power usage of the customers in real-time. Authentication and user privacy preservation are the two major concerns on smart grid security. Authentication schemes that preserve users' privacy from third parties, but not from the power operator, have been proposed. In this paper, we propose a scheme that preserves users' privacy information, including their daily electricity usage pattern from third parties as well as from the power operator. At the same time, the scheme ensures that authentication can be properly done. These two properties are achieved by using anonymous credential under the principle of blind signature. Basically, a customer generates a set of credentials by himself and asks the control center to blindly sign them. When the customer needs to request more power later on, he presents the signed credential to the control center as proof of his identity. Implementation and analysis show that our scheme is feasible in terms of a number of performance measures such as the signing time and the credential collision rate. © 2011 IEEE.published_or_final_versionThe IEEE Global Communications Conference (GLOBECOM 2011), Houston, TX, USA, 5-9 December 2011. In Proceedings of GLOBECOM, 2011, p. 1-

Secure Data Aggregation Mechanism for Water Distribution System using Blockchain

Development of intelligent systems in particular Water Distribution Systems (WDS) increases the demand of implementing a secure scheme that can preserve user’s identification and data consumption through maintaining confidentiality, authentication and integrity. Decentralization topology has investigated a lot recently in the literature with the development of bitcoins and Ethereum networks in different IoT disciplines such as power systems and healthcare systems. In this paper, feasibility and uses cases studies on the integration WDS with Blockchain Technology are discussed. Moreover, the customer’s data and identity anonymity techniques that can be integrated with the network are discussed. Furthermore, a data aggregation mechanism of the smart meters in Water Distribution System (WDS) based on distributed ledger and Blockchain technologies is proposed. Further, the customer’s identity using bloom filter is simulated and optimal parameters of the bloom filter are suggest

Protection of data privacy based on artificial intelligence in Cyber-Physical Systems

With the rapid evolution of cyber attack techniques, the security and privacy of Cyber-Physical Systems (CPSs) have become key challenges. CPS environments have several properties that make them unique in efforts to appropriately secure them when compared with the processes, techniques and processes that have evolved for traditional IT networks and platforms. CPS ecosystems are comprised of heterogeneous systems, each with long lifespans. They use multitudes of operating systems and communication protocols and are often designed without security as a consideration. From a privacy perspective, there are also additional challenges. It is hard to capture and filter the heterogeneous data sources of CPSs, especially power systems, as their data should include network traffic and the sensing data of sensors. Protecting such data during the stages of collection, analysis and publication still open the possibility of new cyber threats disrupting the operational loops of power systems. Moreover, while protecting the original data of CPSs, identifying cyberattacks requires intrusion detection that produces high false alarm rates. This thesis significantly contributes to the protection of heterogeneous data sources, along with the high performance of discovering cyber-attacks in CPSs, especially smart power networks (i.e., power systems and their networks). For achieving high data privacy, innovative privacy-preserving techniques based on Artificial Intelligence (AI) are proposed to protect the original and sensitive data generated by CPSs and their networks. For cyber-attack discovery, meanwhile applying privacy-preserving techniques, new anomaly detection algorithms are developed to ensure high performances in terms of data utility and accuracy detection. The first main contribution of this dissertation is the development of a privacy preservation intrusion detection methodology that uses the correlation coefficient, independent component analysis, and Expectation Maximisation (EM) clustering algorithms to select significant data portions and discover cyber attacks against power networks. Before and after applying this technique, machine learning algorithms are used to assess their capabilities to classify normal and suspicious vectors. The second core contribution of this work is the design of a new privacy-preserving anomaly detection technique protecting the confidential information of CPSs and discovering malicious observations. Firstly, a data pre-processing technique filters and transforms data into a new format that accomplishes the aim of preserving privacy. Secondly, an anomaly detection technique using a Gaussian mixture model which fits selected features, and a Kalman filter technique that accurately computes the posterior probabilities of legitimate and anomalous events are employed. The third significant contribution of this thesis is developing a novel privacy-preserving framework for achieving the privacy and security criteria of smart power networks. In the first module, a two-level privacy module is developed, including an enhanced proof of work technique-based blockchain for accomplishing data integrity and a variational autoencoder approach for changing the data to an encoded data format to prevent inference attacks. In the second module, a long short-term memory deep learning algorithm is employed in anomaly detection to train and validate the outputs from the two-level privacy modules