Achieving differential privacy against non‐intrusive load monitoring in smart grid: A fog computing approach

Fog computing, a non‐trivial extension of cloud computing to the edge of the network, has great advantage in providing services with a lower latency. In smart grid, the application of fog computing can greatly facilitate the collection of consumer's fine‐grained energy consumption data, which can then be used to draw the load curve and develop a plan or model for power generation. However, such data may also reveal customer's daily activities. Non‐intrusive load monitoring (NILM) can monitor an electrical circuit that powers a number of appliances switching on and off independently. If an adversary analyzes the meter readings together with the data measured by an NILM device, the customer's privacy will be disclosed. In this paper, we propose an effective privacy‐preserving scheme for electric load monitoring, which can guarantee differential privacy of data disclosure in smart grid. In the proposed scheme, an energy consumption behavior model based on Factorial Hidden Markov Model (FHMM) is established. In addition, noise is added to the behavior parameter, which is different from the traditional methods that usually add noise to the energy consumption data. The analysis shows that the proposed scheme can get a better trade‐off between utility and privacy compared with other popular methods.

[1]  Xiaojiang Du,et al.  Permission-combination-based scheme for Android mobile malware detection , 2014, 2014 IEEE International Conference on Communications (ICC).

[2]  Patrick D. McDaniel,et al.  Protecting consumer privacy from electric load monitoring , 2011, CCS '11.

[3]  Annabelle Lee,et al.  Guidelines for Smart Grid Cyber Security , 2010 .

[4]  Aaron Roth,et al.  The Algorithmic Foundations of Differential Privacy , 2014, Found. Trends Theor. Comput. Sci..

[5]  Yang Xiang,et al.  Modeling the Propagation of Worms in Networks: A Survey , 2014, IEEE Communications Surveys & Tutorials.

[6]  Cynthia Dwork,et al.  Differential Privacy , 2006, ICALP.

[7]  Jun Zhang,et al.  Modeling Propagation Dynamics of Social Network Worms , 2013, IEEE Transactions on Parallel and Distributed Systems.

[8]  Yang Li,et al.  A Comprehensive Trustworthy Data Collection Approach in Sensor-Cloud Systems , 2022, IEEE Transactions on Big Data.

[9]  Longfei Wu,et al.  MobiFish: A lightweight anti-phishing scheme for mobile phones , 2014, 2014 23rd International Conference on Computer Communication and Networks (ICCCN).

[10]  Hui Tian,et al.  Data collection from WSNs to the cloud based on mobile Fog elements , 2017, Future Gener. Comput. Syst..

[11]  Wanlei Zhou,et al.  Identifying Propagation Sources in Networks: State-of-the-Art and Comparative Studies , 2017, IEEE Communications Surveys & Tutorials.

[12]  Yunlei Zhao,et al.  Privacy-preserving smart metering with regional statistics and personal enquiry services , 2013, ASIA CCS '13.

[14]  Mohsen Guizani,et al.  An effective key management scheme for heterogeneous sensor networks , 2007, Ad Hoc Networks.

[15]  Xiaojiang Du,et al.  A survey of key management schemes in wireless sensor networks , 2007, Comput. Commun..

[16]  Jian Weng,et al.  Cost-Friendly Differential Privacy for Smart Meters: Exploiting the Dual Roles of the Noise , 2017, IEEE Transactions on Smart Grid.

[17]  Paul W. Cuff,et al.  Differential Privacy as a Mutual Information Constraint , 2016, CCS.

[18]  Manish Marwah,et al.  Unsupervised Disaggregation of Low Frequency Power Measurements , 2011, SDM.

[19]  Xiaojiang Du,et al.  Biometric-based two-level secure access control for Implantable Medical Devices during emergencies , 2011, 2011 Proceedings IEEE INFOCOM.

[20]  Xiaojiang Du,et al.  Towards Delay-Tolerant Flexible Data Access Control for Smart Grid with Renewable Energy Resources , 2018, ArXiv.

[21]  Wanlei Zhou,et al.  A Sword with Two Edges: Propagation Studies on Both Positive and Negative Information in Online Social Networks , 2015, IEEE Transactions on Computers.

[22]  Carl Eklund,et al.  National Institute for Standards and Technology , 2009, Encyclopedia of Biometrics.

[23]  Dominik Engel,et al.  Differential privacy for real smart metering data , 2017, Computer Science - Research and Development.

[24]  Xiaohui Liang,et al.  EPPA: An Efficient and Privacy-Preserving Aggregation Scheme for Secure Smart Grid Communications , 2012, IEEE Transactions on Parallel and Distributed Systems.

[25]  G. W. Hart,et al.  Nonintrusive appliance load monitoring , 1992, Proc. IEEE.

[26]  Rajasekhar Mungara,et al.  A Routing-Driven Elliptic Curve Cryptography based Key Management Scheme for Heterogeneous Sensor Networks , 2014 .

[27]  Claude Castelluccia,et al.  I Have a DREAM! (DiffeRentially privatE smArt Metering) , 2011, Information Hiding.

[28]  David K. Y. Yau,et al.  Privacy-Assured Aggregation Protocol for Smart Metering: A Proactive Fault-Tolerant Approach , 2016, IEEE/ACM Transactions on Networking.

[29]  Anfeng Liu,et al.  Fog-based storage technology to fight with cyber threat , 2018, Future Gener. Comput. Syst..

[30]  Ivan Stojmenovic,et al.  An overview of Fog computing and its security issues , 2016, Concurr. Comput. Pract. Exp..

[31]  Yan Zhang,et al.  Software Defined Machine-to-Machine Communication for Smart Energy Management , 2017, IEEE Communications Magazine.

[32]  Xiaojiang Du,et al.  Security in wireless sensor networks , 2008, IEEE Wireless Communications.

[33]  Cynthia Dwork,et al.  Calibrating Noise to Sensitivity in Private Data Analysis , 2006, TCC.

[34]  Xiaojiang Du,et al.  Achieving Efficient and Secure Data Acquisition for Cloud-Supported Internet of Things in Smart Grid , 2017, IEEE Internet of Things Journal.

[35]  H. Vincent Poor,et al.  Smart Meter Privacy: A Theoretical Framework , 2013, IEEE Transactions on Smart Grid.

[36]  José M. F. Moura,et al.  Event detection for Non Intrusive load monitoring , 2012, IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society.

[37]  Ashwin Machanavajjhala,et al.  No free lunch in data privacy , 2011, SIGMOD '11.

[38]  Mohsen Guizani,et al.  Toward Delay-Tolerant Flexible Data Access Control for Smart Grid With Renewable Energy Resources , 2017, IEEE Transactions on Industrial Informatics.

[39]  Mohsen Guizani,et al.  Transactions papers a routing-driven Elliptic Curve Cryptography based key management scheme for Heterogeneous Sensor Networks , 2009, IEEE Transactions on Wireless Communications.

[40]  Frank McSherry,et al.  Privacy integrated queries: an extensible platform for privacy-preserving data analysis , 2009, SIGMOD Conference.

[41]  Haimonti Dutta,et al.  NILMTK: an open source toolkit for non-intrusive load monitoring , 2014, e-Energy.

[42]  Giacomo Verticale,et al.  Evaluation of the Precision-Privacy Tradeoff of Data Perturbation for Smart Metering , 2015, IEEE Transactions on Smart Grid.

[43]  Prasad Diwane Achieving Big Data Privacy via Hybrid Cloud , 2017 .

[44]  Michael I. Jordan,et al.  Factorial Hidden Markov Models , 1995, Machine Learning.

[45]  Andrey Brito,et al.  A Technique to provide differential privacy for appliance usage in smart metering , 2016, Inf. Sci..

[46]  G Ramesh,et al.  Security Threats to Mobile Multimedia Applications: Camera-Based Attacks on Mobile Phones , 2018 .

[47]  Jiming Chen,et al.  Diverse Grouping-Based Aggregation Protocol With Error Detection for Smart Grid Communications , 2015, IEEE Transactions on Smart Grid.

[48]  J. Zico Kolter,et al.  REDD : A Public Data Set for Energy Disaggregation Research , 2011 .