{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T19:17:01Z","timestamp":1781032621522,"version":"3.54.1"},"reference-count":51,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2020,7,8]],"date-time":"2020-07-08T00:00:00Z","timestamp":1594166400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100008982","name":"Qatar National Research Fund","doi-asserted-by":"publisher","award":["NPRP10-0101-170082"],"award-info":[{"award-number":["NPRP10-0101-170082"]}],"id":[{"id":"10.13039\/100008982","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Deep learning models have been applied for varied electrical applications in smart grids with a high degree of reliability and accuracy. The development of deep learning models requires the historical data collected from several electric utilities during the training of the models. The lack of historical data for training and testing of developed models, considering security and privacy policy restrictions, is considered one of the greatest challenges to machine learning-based techniques. The paper proposes the use of homomorphic encryption, which enables the possibility of training the deep learning and classical machine learning models whilst preserving the privacy and security of the data. The proposed methodology is tested for applications of fault identification and localization, and load forecasting in smart grids. The results for fault localization show that the classification accuracy of the proposed privacy-preserving deep learning model while using homomorphic encryption is 97\u201398%, which is close to 98\u201399% classification accuracy of the model on plain data. Additionally, for load forecasting application, the results show that RMSE using the homomorphic encryption model is 0.0352 MWh while RMSE without application of encryption in modeling is around 0.0248 MWh.<\/jats:p>","DOI":"10.3390\/info11070357","type":"journal-article","created":{"date-parts":[[2020,7,8]],"date-time":"2020-07-08T11:47:46Z","timestamp":1594208866000},"page":"357","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Privacy Preservation of Data-Driven Models in Smart Grids Using Homomorphic Encryption"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9431-4849","authenticated-orcid":false,"given":"Dabeeruddin","family":"Syed","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Texas A&amp;M University, College Station, TX 77843, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9392-6141","authenticated-orcid":false,"given":"Shady S.","family":"Refaat","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Texas A&amp;M University at Qatar, Education City, Doha 23874, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7139-7322","authenticated-orcid":false,"given":"Othmane","family":"Bouhali","sequence":"additional","affiliation":[{"name":"Research Computing, Texas A&amp;M University at Qatar, Education City, Doha 23874, Qatar"},{"name":"Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha 5825, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"710","DOI":"10.1016\/j.rser.2016.01.011","article-title":"A review of the development of Smart Grid technologies","volume":"59","author":"Tuballa","year":"2016","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1186\/s42162-018-0007-5","article-title":"Big data analytics in smart grids: A review","volume":"1","author":"Zhang","year":"2018","journal-title":"Energy Inform."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Efthymiou, C., and Kalogridis, G. (2010, January 4\u20136). Smart grid privacy via anonymization of smart metering data. Proceedings of the 2010 First IEEE International Conference on Smart Grid Communications, Gaithersburg, MD, USA.","DOI":"10.1109\/SMARTGRID.2010.5622050"},{"key":"ref_4","unstructured":"Raschka, S., and Mirjalili, V. (2019). Python Machine Learning: Machine Learning and Deep Learning with Python, Scikit-Learn, and TensorFlow 2, Packt Publishing Ltd."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1093\/nsr\/nwx110","article-title":"Deep learning for natural language processing: Advantages and challenges","volume":"5","author":"Li","year":"2018","journal-title":"Natl. Sci. Rev."},{"key":"ref_6","unstructured":"Gjoreski, H., Bizjak, J., Gjoreski, M., and Gams, M. (2016, January 9\u201315). Comparing deep and classical machine learning methods for human activity recognition using wrist accelerometer. Proceedings of the IJCAI 2016 Workshop on Deep Learning for Artificial Intelligence, New York, NY, USA."},{"key":"ref_7","unstructured":"(2020, July 03). Global Smart Meter Total to Double by 2024 with Asia in the Lead. Available online: https:\/\/www.woodmac.com\/news\/editorial\/global-smart-meter-total-h1-2019\/."},{"key":"ref_8","unstructured":"Consulting, C. (2020, July 03). Big Data BlackOut: Are Utilities Powering Up Their Data Analytics?. Available online: https:\/\/www.capgemini.com\/wp-content\/uploads\/2017\/07\/big_data_blackout-_are_utilities_powering_up_their_data_analytics.pdf."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Bost, R., Popa, R.A., Tu, S., and Goldwasser, S. (2015, January 8\u201311). Machine learning classification over encrypted data. Proceedings of the Annual Network and Distributed System Security Symposium (NDSS), San Diego, CA, USA.","DOI":"10.14722\/ndss.2015.23241"},{"key":"ref_10","first-page":"169","article-title":"On data banks and privacy homomorphisms","volume":"4","author":"Rivest","year":"1978","journal-title":"Found. Secur. Comput."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ajtai, M., and Dwork, C. (1997, January 4\u20136). A public-key cryptosystem with worst-case\/average-case equivalence. Proceedings of the Twenty-Ninth Annual ACM Symposium on Theory of Computing, El Paso, TX, USA.","DOI":"10.1145\/258533.258604"},{"key":"ref_12","unstructured":"Gentry, C. (June, January 31). Fully homomorphic encryption using ideal lattices. Proceedings of the Forty-First Annual ACM Symposium on Theory of Computing, Bethesda, MD, USA."},{"key":"ref_13","unstructured":"Gilad-Bachrach, R., Dowlin, N., Laine, K., Lauter, K., Naehrig, M., and Wernsing, J. (2016, January 20\u201322). Cryptonets: Applying neural networks to encrypted data with high throughput and accuracy. Proceedings of the International Conference on Machine Learning, New York, NY, USA."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Bourse, F., Minelli, M., Minihold, M., and Paillier, P. (2018). Fast homomorphic evaluation of deep discretized neural networks. Annual International Cryptology Conference, Springer.","DOI":"10.1007\/978-3-319-96878-0_17"},{"key":"ref_15","unstructured":"Carlini, N., Liu, C., Erlingsson, \u00da., Kos, J., and Song, D. (2019, January 14\u201316). The secret sharer: Evaluating and testing unintended memorization in neural networks. Proceedings of the 28th {USENIX} Security Symposium ({USENIX} Security 19), Santa Clara, CA, USA."},{"key":"ref_16","unstructured":"Rajkumar, A., and Agarwal, S. (2012, January 21\u201323). A differentially private stochastic gradient descent algorithm for multiparty classification. Proceedings of the Artificial Intelligence and Statistics, La Palma, Spain."},{"key":"ref_17","unstructured":"Papernot, N., Song, S., Mironov, I., Raghunathan, A., Talwar, K., and Erlingsson, \u00da. (2018). Scalable private learning with pate. arXiv."},{"key":"ref_18","unstructured":"Smith, V., Chiang, C.K., Sanjabi, M., and Talwalkar, A.S. (2017, January 4\u20139). Federated multi-task learning. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_19","unstructured":"Takabi, H., Hesamifard, E., and Ghasemi, M. (2016, January 5\u201310). Privacy preserving multi-party machine learning with homomorphic encryption. Proceedings of the 29th Annual Conference on Neural Information Processing Systems (NIPS), Barcelona, Spain."},{"key":"ref_20","unstructured":"Gilad-Bachrach, R., Finley, T.W., Bilenko, M., and Xie, P. (2018). Neural Networks for Encrypted Data. (9,946,970), U.S. Patent."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1515\/popets-2018-0024","article-title":"Privacy-preserving machine learning as a service","volume":"2018","author":"Hesamifard","year":"2018","journal-title":"Proc. Priv. Enhancing Technol."},{"key":"ref_22","first-page":"270","article-title":"New privacy preserving back propagation learning for secure multiparty computation","volume":"43","author":"Miyajima","year":"2016","journal-title":"IAENG Int. J. Comput. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"136481","DOI":"10.1109\/ACCESS.2019.2940052","article-title":"Efficient privacy-preserving machine learning for blockchain network","volume":"7","author":"Kim","year":"2019","journal-title":"IEEE Access"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3339474","article-title":"Federated machine learning: Concept and applications","volume":"10","author":"Yang","year":"2019","journal-title":"ACM Trans. Intell. Syst. Technol. (TIST)"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1109\/MSEC.2018.2888775","article-title":"Privacy-preserving machine learning: Threats and solutions","volume":"17","author":"Chang","year":"2019","journal-title":"IEEE Secur. Priv."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Abadi, M., Chu, A., Goodfellow, I., McMahan, H.B., Mironov, I., Talwar, K., and Zhang, L. (2016, January 24\u201328). Deep learning with differential privacy. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, Vienna, Austria.","DOI":"10.1145\/2976749.2978318"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Truex, S., Baracaldo, N., Anwar, A., Steinke, T., Ludwig, H., Zhang, R., and Zhou, Y. (2019, January 13). A hybrid approach to privacy-preserving federated learning. Proceedings of the 12th ACM Workshop on Artificial Intelligence and Security, Orlando, FL, USA.","DOI":"10.1145\/3338501.3357370"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Kuri, S., Hayashi, T., Omori, T., Ozawa, S., Aono, Y., Wang, L., and Moriai, S. (December, January 27). Privacy preserving extreme learning machine using additively homomorphic encryption. Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence (SSCI), Honolulu, HI, USA.","DOI":"10.1109\/SSCI.2017.8285190"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.ins.2018.05.005","article-title":"Privacy preserving multi-party computation delegation for deep learning in cloud computing","volume":"459","author":"Ma","year":"2018","journal-title":"Inf. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Shokri, R., and Shmatikov, V. (2015, January 12\u201316). Privacy-preserving deep learning. Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, Denver, CO, USA.","DOI":"10.1145\/2810103.2813687"},{"key":"ref_31","unstructured":"Van Dijk, M., Gentry, C., Halevi, S., and Vaikuntanathan, V. (June, January 30). Fully homomorphic encryption over the integers. Proceedings of the Annual International Conference on the Theory and Applications of Cryptographic Techniques, French Riviera, France."},{"key":"ref_32","unstructured":"Brenner, M., Dai, W., Halevi, S., Han, K., Jalali, A., Kim, M., Laine, K., Malozemoff, A., Paillier, P., and Polyakov, Y. (2017). A Standard API for RLWE-Based Homomorphic Encryption, HomomorphicEncryption.org. Technical Report."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Tsiounis, Y., and Yung, M. (1998). On the security of ElGamal based encryption. International Workshop on Public Key Cryptography, Springer.","DOI":"10.1007\/BFb0054019"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Patel, N., Oza, P., and Agrawal, S. (2019). Homomorphic Cryptography and Its Applications in Various Domains. International Conference on Innovative Computing and Communications, Springer.","DOI":"10.1007\/978-981-13-2324-9_27"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"El Makkaoui, K., Ezzati, A., and Beni-Hssane, A. (2016, January 10\u201311). Securely adapt a Paillier encryption scheme to protect the data confidentiality in the cloud environment. Proceedings of the International Conference on Big Data and Advanced Wireless Technologies, Blagoevgrad, Bulgaria.","DOI":"10.1145\/3010089.3016026"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Halevi, S., and Shoup, V. (2014, January 17\u201321). Algorithms in helib. Proceedings of the Annual Cryptology Conference, Santa Barbara, CA, USA.","DOI":"10.1007\/978-3-662-44371-2_31"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Dai, W., and Sunar, B. (2015, January 3\u20134). CuHE: A homomorphic encryption accelerator library. Proceedings of the International Conference on Cryptography and Information Security in the Balkans, Koper, Slovenia.","DOI":"10.1007\/978-3-319-29172-7_11"},{"key":"ref_38","unstructured":"Chillotti, I., Gama, N., Georgieva, M., and Izabach\u00e8ne, M. (2012, March 15). TFHE: Fast Fully Homomorphic Encryption Library, August 2016. Available online: https:\/\/tfhe.github.io\/tfhe\/."},{"key":"ref_39","unstructured":"Aguilar-Melchor, C., Barrier, J., Guelton, S., Guinet, A., Killijian, M.O., and Lepoint, T. (March, January 29). NFLlib: NTT-based fast lattice library. Proceedings of the Cryptographers\u2019 Track at the RSA Conference, San Francisco, CA, USA."},{"key":"ref_40","unstructured":"Laine, K., and Player, R. (2016). Simple Encrypted Arithmetic Library-Seal (v2. 0), Microsoft Research. Technical Report."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Crockett, E., and Peikert, C. (2016, January 24\u201328). \u039bo\u03bb: Functional Lattice Cryptography. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, Vienna, Austria.","DOI":"10.1145\/2976749.2978402"},{"key":"ref_42","unstructured":"Polyakov, Y., Rohloff, K., and Ryan, G.W. (2017). PALISADE Lattice Cryptography Library User Manual, Cybersecurity Research Center, New Jersey Institute ofTechnology (NJIT). Technical Report."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Takagi, T., and Peyrin, T. (2017, January 3\u20137). Advances in Cryptology\u2013ASIACRYPT 2017. Proceedings of the 23rd International Conference on the Theory and Applications of Cryptology and Information Security, Hong Kong, China.","DOI":"10.1007\/978-3-319-70697-9"},{"key":"ref_44","unstructured":"(2020, July 03). Lattigo 1.3.1. Available online: http:\/\/github.com\/ldsec\/lattigo."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Zainab, A., Refaat, S.S., Syed, D., Ghrayeb, A., and Abu-Rub, H. (2019, January 9\u201312). Faulted Line Identification and Localization in Power System using Machine Learning Techniques. Proceedings of the 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA.","DOI":"10.1109\/BigData47090.2019.9006377"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"4640","DOI":"10.1109\/TPWRS.2019.2917794","article-title":"Real-Time Faulted Line Localization and PMU Placement in Power Systems Through Convolutional Neural Networks","volume":"34","author":"Li","year":"2019","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_47","unstructured":"(2020, July 03). Dark Sky API\u2014Weather Conditions. Available online: https:\/\/darksky.net\/dev\/."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Syed, D., Refaat, S.S., Abu-Rub, H., Bouhali, O., Zainab, A., and Xie, L. (2019, January 9\u201312). Averaging Ensembles Model for Forecasting of Short-term Load in Smart Grids. Proceedings of the 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA.","DOI":"10.1109\/BigData47090.2019.9006183"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2633600","article-title":"(Leveled) fully homomorphic encryption without bootstrapping","volume":"6","author":"Brakerski","year":"2014","journal-title":"ACM Trans. Comput. Theory (TOCT)"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Domingo-Ferrer, J. (2002). A provably secure additive and multiplicative privacy homomorphism. International Conference on Information Security, Springer.","DOI":"10.1007\/3-540-45811-5_37"},{"key":"ref_51","unstructured":"Soykan, E.U., Bilgin, Z., Ersoy, M.A., and Tomur, E. (2019, January 9\u201313). Differentially Private Deep Learning for Load Forecasting on Smart Grid. Proceedings of the 2019 IEEE Globecom Workshops (GC Wkshps), Waikoloa, HI, USA."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/11\/7\/357\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:49:06Z","timestamp":1760176146000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/11\/7\/357"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,8]]},"references-count":51,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2020,7]]}},"alternative-id":["info11070357"],"URL":"https:\/\/doi.org\/10.3390\/info11070357","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,8]]}}}