{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T04:05:31Z","timestamp":1742961931830,"version":"3.40.3"},"publisher-location":"Cham","reference-count":39,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031096396"},{"type":"electronic","value":"9783031096402"}],"license":[{"start":{"date-parts":[[2022,7,6]],"date-time":"2022-07-06T00:00:00Z","timestamp":1657065600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,7,6]],"date-time":"2022-07-06T00:00:00Z","timestamp":1657065600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-09640-2_15","type":"book-chapter","created":{"date-parts":[[2022,11,18]],"date-time":"2022-11-18T08:29:41Z","timestamp":1668760181000},"page":"323-343","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Preserving the Privacy and Cybersecurity of Home Energy Data"],"prefix":"10.1007","author":[{"given":"Richard","family":"Bean","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanjun","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ryan K. \u00a0L.","family":"Ko","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinyu","family":"Mao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guangdong","family":"Bai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,7,6]]},"reference":[{"key":"15_CR1","unstructured":"Australian Government Clean Energy Regulator, Small-scale renewable energy scheme (2018). http:\/\/www.cleanenergyregulator.gov.au\/RET\/About-the-Renewable-Energy-Target\/How-the-scheme-works\/Small-scale-Renewable-Energy-Scheme. [Retrieved: December, 2021]"},{"issue":"4","key":"15_CR2","doi-asserted-by":"publisher","first-page":"922","DOI":"10.1111\/1467-8489.12319","volume":"63","author":"R Best","year":"2019","unstructured":"R. Best, P.J. Burke, S. Nishitateno, Understanding the determinants of rooftop solar installation: evidence from household surveys in Australia. Aust. J. Agric. Resour. Econ. 63(4), 922\u2013939 (2019)","journal-title":"Aust. J. Agric. Resour. Econ."},{"key":"15_CR3","unstructured":"Australian Energy Market Commission, Five minute settlement (2021). https:\/\/www.aemc.gov.au\/rule-changes\/five-minute-settlement. [Retrieved: December, 2021]"},{"key":"15_CR4","doi-asserted-by":"crossref","unstructured":"A.S. Spanias, Solar energy management as an internet of things (iot) application, in 2017 8th International Conference on Information, Intelligence, Systems & Applications (IISA) (IEEE, 2017), pp. 1\u20134","DOI":"10.1109\/IISA.2017.8316460"},{"key":"15_CR5","doi-asserted-by":"publisher","first-page":"54992","DOI":"10.1109\/ACCESS.2021.3071654","volume":"9","author":"D Syed","year":"2021","unstructured":"D. Syed, H. Abu-Rub, A. Ghrayeb, S.S. Refaat, M. Houchati, O. Bouhali, S. Ba\u00f1ales, Deep learning-based short-term load forecasting approach in smart grid with clustering and consumption pattern recognition. IEEE Access 9, 54992\u201355008 (2021)","journal-title":"IEEE Access"},{"key":"15_CR6","unstructured":"Australia Energy Market Operator, Solar and wind energy forecasting (2016). http:\/\/www.aemo.com.au\/Electricity\/National-Electricity-Market-NEM\/Planning-and-forecasting\/Solar-and-wind-energy-forecasting. [Retrieved: December, 2021]"},{"key":"15_CR7","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1016\/j.enbuild.2018.11.025","volume":"183","author":"R Razavi","year":"2019","unstructured":"R. Razavi, A. Gharipour, M. Fleury, I.J. Akpan, Occupancy detection of residential buildings using smart meter data: A large-scale study. Energy Buildings 183, 195\u2013208 (2019)","journal-title":"Energy Buildings"},{"key":"15_CR8","unstructured":"Australian Photovoltaic Institute, Mapping Australian photovoltaic installations (2021). https:\/\/pv-map.apvi.org.au\/historical. [Retrieved: December, 2021]"},{"key":"15_CR9","doi-asserted-by":"publisher","first-page":"35411","DOI":"10.1109\/ACCESS.2021.3057525","volume":"9","author":"I Yilmaz","year":"2021","unstructured":"I. Yilmaz, A. Siraj, Avoiding occupancy detection from smart meter using adversarial machine learning. IEEE Access 9, 35411\u201335430 (2021)","journal-title":"IEEE Access"},{"key":"15_CR10","doi-asserted-by":"crossref","unstructured":"D. Chen, D. Irwin, Weatherman: Exposing weather-based privacy threats in big energy data, in 2017 IEEE International Conference on Big Data (Big Data) (IEEE, 2017), pp. 1079\u20131086","DOI":"10.1109\/BigData.2017.8258032"},{"key":"15_CR11","doi-asserted-by":"crossref","unstructured":"D. Chen, S. Iyengar, D. Irwin, P. Shenoy, Sunspot: Exposing the location of anonymous solar-powered homes, in Proceedings of the 3rd ACM International Conference on Systems for Energy-Efficient Built Environments, pp. 85\u201394 (2016)","DOI":"10.1145\/2993422.2993573"},{"key":"15_CR12","first-page":"22","volume":"151","author":"B Raoult","year":"2017","unstructured":"B. Raoult, C. Bergeron, A.L. Al\u00f3s, J.-N. Th\u00e9paut, D. Dee, Climate service develops user-friendly data store. ECMWF Newsletter 151, 22\u201327 (2017)","journal-title":"ECMWF Newsletter"},{"key":"15_CR13","doi-asserted-by":"crossref","unstructured":"W.F. Holmgren, C.W. Hansen, M.A. Mikofski, pvlib python: A python package for modeling solar energy systems. J. Open Source Softw. 3(29), 884 (2018)","DOI":"10.21105\/joss.00884"},{"issue":"730","key":"15_CR14","doi-asserted-by":"publisher","first-page":"1999","DOI":"10.1002\/qj.3803","volume":"146","author":"H Hersbach","year":"2020","unstructured":"H. Hersbach, B. Bell, P. Berrisford, S. Hirahara, A. Hor\u00e1nyi, J. Mu\u00f1oz-Sabater, J. Nicolas, C. Peubey, R. Radu, D. Schepers, et al., The era5 global reanalysis. Q. J. Roy. Meteorol. Soc. 146(730), 1999\u20132049 (2020)","journal-title":"Q. J. Roy. Meteorol. Soc."},{"key":"15_CR15","unstructured":"L.R. Camargo, J. Schmidt, Simulation of long-term time series of solar photovoltaic power: is the era5-land reanalysis the next big step? Preprint (2020). arXiv:2003.04131"},{"key":"15_CR16","doi-asserted-by":"publisher","first-page":"503","DOI":"10.1016\/j.rser.2013.02.027","volume":"23","author":"AK Yadav","year":"2013","unstructured":"A.K. Yadav, S. Chandel, Tilt angle optimization to maximize incident solar radiation: A review. Renew. Sustain. Energy Rev. 23, 503\u2013513 (2013)","journal-title":"Renew. Sustain. Energy Rev."},{"key":"15_CR17","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/B978-0-12-801595-7.00005-7","volume-title":"The Cloud Security Ecosystem","author":"MA Will","year":"2015","unstructured":"M.A. Will, R.K. Ko, A guide to homomorphic encryption, in The Cloud Security Ecosystem, ed. by R. Ko, K.-K. R. Choo (Syngress, Boston, 2015), pp. 101\u2013127"},{"key":"15_CR18","doi-asserted-by":"crossref","unstructured":"M.A. Will, B. Nicholson, M. Tiehuis, R.K. Ko, Secure voting in the cloud using homomorphic encryption and mobile agents, in 2015 International Conference on Cloud Computing Research and Innovation (ICCCRI), pp. 173\u2013184 (2015)","DOI":"10.1109\/ICCCRI.2015.30"},{"key":"15_CR19","doi-asserted-by":"crossref","unstructured":"M.A. Will, R.K. Ko, I.H. Witten, Privacy preserving computation by fragmenting individual bits and distributing gates, in 2016 IEEE Trustcom\/BigDataSE\/ISPA, pp. 900\u2013908 (2016)","DOI":"10.1109\/TrustCom.2016.0154"},{"key":"15_CR20","doi-asserted-by":"publisher","first-page":"399","DOI":"10.1007\/978-3-030-58951-6_20","volume-title":"Computer Security \u2013 ESORICS 2020","author":"Y Zhang","year":"2020","unstructured":"Y. Zhang, G. Bai, X. Li, C. Curtis, C. Chen, R.K.L. Ko, Privcoll: Practical privacy-preserving collaborative machine learning, in Computer Security \u2013 ESORICS 2020, ed. by L. Chen, N. Li, K. Liang, S. Schneider (Springer International Publishing, Cham, 2020), pp. 399\u2013418"},{"key":"15_CR21","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1007\/978-3-030-88428-4_20","volume-title":"Computer Security \u2013 ESORICS 2021","author":"Y Zhang","year":"2021","unstructured":"Y. Zhang, G. Bai, X. Li, C. Curtis, C. Chen, R.K.L. Ko, Privacy-preserving gradient descent for distributed genome-wide analysis, in Computer Security \u2013 ESORICS 2021, ed. by E. Bertino, H. Shulman, M. Waidner (Springer International Publishing, Cham, 2021), pp. 395\u2013416"},{"key":"15_CR22","doi-asserted-by":"crossref","unstructured":"C. Dwork, Differential privacy: A survey of results, in International Conference on Theory and Applications of Models of Computation (Springer, 2008), pp. 1\u201319","DOI":"10.1007\/978-3-540-79228-4_1"},{"key":"15_CR23","doi-asserted-by":"crossref","unstructured":"M. Abadi, A. Chu, I. Goodfellow, H.B. McMahan, I. Mironov, K. Talwar, L. Zhang, Deep learning with differential privacy, in Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, pp. 308\u2013318 (2016)","DOI":"10.1145\/2976749.2978318"},{"key":"15_CR24","doi-asserted-by":"crossref","unstructured":"C. Dwork, F. McSherry, K. Nissim, A. Smith, Calibrating noise to sensitivity in private data analysis, in Theory of Cryptography Conference (Springer, 2006), pp. 265\u2013284","DOI":"10.1007\/11681878_14"},{"key":"15_CR25","doi-asserted-by":"crossref","unstructured":"F. McSherry, K. Talwar, Mechanism design via differential privacy, in 48th Annual IEEE Symposium on Foundations of Computer Science (FOCS\u201907) (IEEE, 2007), pp. 94\u2013103","DOI":"10.1109\/FOCS.2007.66"},{"issue":"8","key":"15_CR26","doi-asserted-by":"publisher","first-page":"1619","DOI":"10.1109\/TKDE.2017.2697856","volume":"29","author":"T Zhu","year":"2017","unstructured":"T. Zhu, G. Li, W. Zhou, S.Y. Philip, Differentially private data publishing and analysis: A survey. IEEE Trans. Knowl. Data Eng. 29(8), 1619\u20131638 (2017)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"15_CR27","doi-asserted-by":"crossref","unstructured":"Q. Yang, Y. Liu, T. Chen, Y. Tong, Federated machine learning: Concept and applications. ACM Trans. Intell. Syst. Technol. (TIST) 10(2), 1\u201319 (2019)","DOI":"10.1145\/3298981"},{"key":"15_CR28","unstructured":"P. Kairouz, H.B. McMahan, B. Avent, A. Bellet, M. Bennis, A.N. Bhagoji, K. Bonawitz, Z. Charles, G. Cormode, R. Cummings, et al., Advances and open problems in federated learning. Preprint (2019). arXiv:1912.04977"},{"key":"15_CR29","unstructured":"B. McMahan, E. Moore, D. Ramage, S. Hampson, B. A. y. Arcas, Communication-efficient learning of deep networks from decentralized data, in Artificial Intelligence and Statistics (PMLR, 2017), pp. 1273\u20131282"},{"key":"15_CR30","doi-asserted-by":"crossref","unstructured":"A. Acar, H. Aksu, A.S. Uluagac, M. Conti, A survey on homomorphic encryption schemes: Theory and implementation. ACM Comput. Surv. (CSUR) 51(4), 1\u201335 (2018)","DOI":"10.1145\/3214303"},{"issue":"3","key":"15_CR31","doi-asserted-by":"publisher","first-page":"2086","DOI":"10.1109\/TSG.2016.2606490","volume":"9","author":"K Abdulla","year":"2016","unstructured":"K. Abdulla, J. De Hoog, V. Muenzel, F. Suits, K. Steer, A. Wirth, S. Halgamuge, Optimal operation of energy storage systems considering forecasts and battery degradation. IEEE Trans. Smart Grid 9(3), 2086\u20132096 (2016)","journal-title":"IEEE Trans. Smart Grid"},{"issue":"10","key":"15_CR32","doi-asserted-by":"publisher","first-page":"2388","DOI":"10.1016\/j.renene.2010.03.004","volume":"35","author":"BO Bilal","year":"2010","unstructured":"B.O. Bilal, V. Sambou, P. Ndiaye, C. K\u00e9b\u00e9, M. Ndongo, Optimal design of a hybrid solar\u2013wind-battery system using the minimization of the annualized cost system and the minimization of the loss of power supply probability (LPSP). Renewable Energy 35(10), 2388\u20132390 (2010)","journal-title":"Renewable Energy"},{"issue":"2","key":"15_CR33","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1109\/60.507648","volume":"11","author":"BS Borowy","year":"1996","unstructured":"B.S. Borowy, Z.M. Salameh, Methodology for optimally sizing the combination of a battery bank and PV array in a wind\/PV hybrid system. IEEE Trans. Energy Convers. 11(2), 367\u2013375 (1996)","journal-title":"IEEE Trans. Energy Convers."},{"key":"15_CR34","unstructured":"R. Bean, H. Khan, Using solar and load predictions in battery scheduling at the residential level, in Proceedings of the 8th Solar Integration Workshop, Stockholm 2018 (2018)"},{"key":"15_CR35","doi-asserted-by":"crossref","unstructured":"L. Zhu, S. Han, Deep leakage from gradients, in Federated Learning (Springer, 2020), pp. 17\u201331","DOI":"10.1007\/978-3-030-63076-8_2"},{"key":"15_CR36","doi-asserted-by":"crossref","unstructured":"V. Shejwalkar, A. Houmansadr, Manipulating the byzantine: Optimizing model poisoning attacks and defenses for federated learning. Internet Society, 18 (2021)","DOI":"10.14722\/ndss.2021.24498"},{"key":"15_CR37","doi-asserted-by":"publisher","first-page":"3454","DOI":"10.1109\/TIFS.2020.2988575","volume":"15","author":"K Wei","year":"2020","unstructured":"K. Wei, J. Li, M. Ding, C. Ma, H.H. Yang, F. Farokhi, S. Jin, T.Q. Quek, H.V. Poor, Federated learning with differential privacy: Algorithms and performance analysis. IEEE Trans. Inf. Forensics Secur. 15, 3454\u20133469 (2020)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"issue":"14","key":"15_CR38","doi-asserted-by":"publisher","first-page":"5419","DOI":"10.1175\/JCLI-D-16-0758.1","volume":"30","author":"R Gelaro","year":"2017","unstructured":"R. Gelaro, W. McCarty, M.J. Su\u00e1rez, R. Todling, A. Molod, L. Takacs, C.A. Randles, A. Darmenov, M.G. Bosilovich, R. Reichle, et al., The modern-era retrospective analysis for research and applications, version 2 (merra-2). J. Climate 30(14), 5419\u20135454 (2017)","journal-title":"J. Climate"},{"issue":"3-4","key":"15_CR39","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1561\/0400000042","volume":"9","author":"C Dwork","year":"2014","unstructured":"C. Dwork, A. Roth, et al., The algorithmic foundations of differential privacy. Found. Trends Theor. Comput. Sci. 9(3-4), 211\u2013407 (2014)","journal-title":"Found. Trends Theor. Comput. Sci."}],"container-title":["Emerging Trends in Cybersecurity Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-09640-2_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,18]],"date-time":"2023-07-18T08:18:15Z","timestamp":1689668295000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-09640-2_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,6]]},"ISBN":["9783031096396","9783031096402"],"references-count":39,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-09640-2_15","relation":{},"subject":[],"published":{"date-parts":[[2022,7,6]]},"assertion":[{"value":"6 July 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}