Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Citation - WoS: 16Citation - Scopus: 25A Privacy-Preserving Scheme for Smart Grid Using Trusted Execution Environment(IEEE, 2023) Akgün, Mete; Üstündağ Soykan, Elif; Soykan, GürkanThe increasing transformation from the legacy power grid to the smart grid brings new opportunities and challenges to power system operations. Bidirectional communications between home-area devices and the distribution system empower smart grid functionalities. More granular energy consumption data flows through the grid and enables better smart grid applications. This may also lead to privacy violations since the data can be used to infer the consumer's residential behavior, so-called power signature. Energy utilities mostly aggregate the data, especially if the data is shared with stakeholders for the management of market operations. Although this is a privacy-friendly approach, recent works show that this does not fully protect privacy. On the other hand, some applications, like nonintrusive load monitoring, require disaggregated data. Hence, the challenging problem is to find an efficient way to facilitate smart grid operations without sacrificing privacy. In this paper, we propose a privacy-preserving scheme that leverages consumer privacy without reducing accuracy for smart grid applications like load monitoring. In the proposed scheme, we use a trusted execution environment (TEE) to protect the privacy of the data collected from smart appliances (SAs). The scheme allows customer-oriented smart grid applications as the scheme does not use regular aggregation methods but instead uses customer-oriented aggregation to provide privacy. Hence the accuracy loss stemming from disaggregation is prevented. Our scheme protects the transferred consumption data all the way from SAs to Utility so that possible false data injection attacks on the smart meter that aims to deceive the energy request from the grid are also prevented. We conduct security and game-based privacy analysis under the threat model and provide performance analysis of our implementation. Our results demonstrate that the proposed method overperforms other privacy methods in terms of communication and computation cost. The execution time of aggregation for 10,000 customers, each has 20 SAs is approximately 1 second. The decryption operations performed on the TEE have a linear complexity e.g., 172800 operations take around 1 second while 1728000 operations take around 10 seconds. These results can scale up using cloud or hyper-scalers for real-world applications as our scheme performs offline aggregation.Article Citation - WoS: 1Citation - Scopus: 4A Practical Privacy-Preserving Targeted Advertising Scheme for Iptv Users(Springer Verlag, 2016) Khayati, Leyli Javid; Örencik, Cengiz; Savaş, Erkay; Ustaoğlu, BerkantIn this work, we present a privacy-preserving scheme for targeted advertising via the Internet Protocol TV (IPTV). The scheme uses a communication model involving a collection of subscribers, a content provider (IPTV), advertisers and a semi-trusted server. To target potential customers, the advertiser can utilize not only demographic information of subscribers, but also their watching habits. The latter includes watching history, preferences for IPTV content and watching rate, which are periodically (e.g., weekly) published on a semi-trusted server (e.g., cloud server) along with anonymized demographics. Since the published data may leak sensitive information about subscribers, it is safeguarded using cryptographic techniques in addition to the anonymization of demographics. The techniques used by the advertiser, which can be manifested in its queries to the server, are considered (trade) secrets and therefore are protected as well. The server is oblivious to the published data and the queries of the advertiser as well as its own responses to these queries. Only a legitimate advertiser, endorsed with so-called trapdoors by the IPTV, can query the cloud server and access the query results. Even when some background information about users is available, query responses do not leak sensitive information about the IPTV users. The performance of the proposed scheme is evaluated with experiments, which show that the scheme is practical. The algorithms demonstrate both weak and strong scaling property and take advantage of high level of parallelism. The scheme can also be applied as a recommendation system. © 2015, Springer-Verlag Berlin Heidelberg.