skip to main content
10.1145/2899015.2899021acmconferencesArticle/Chapter ViewAbstractPublication Pagesasia-ccsConference Proceedingsconference-collections
research-article

BES: Differentially Private and Distributed Event Aggregation in Advanced Metering Infrastructures

Published: 30 May 2016 Publication History

Abstract

Significant challenges for online event aggregation in the context of Cyber-Physical Systems stem from the computational requirements of their distributed nature, as well as from their privacy concerns. In the context of the latter, differential privacy has gained popularity because of its strong privacy protection guarantees, holding against very powerful adversaries. Despite such strong guarantees, though, its adoption in real-world applications is limited by the privacy-preserving noise it introduces to the analysis, which might compromise its usefulness. We investigate the above problem from a system-perspective in the context of Advanced Metering Infrastructures, providing strong privacy guarantees together with useful results for event aggregation taking into account the distributed nature of such systems. We present a streaming-based framework, Bes, and propose methods to limit the noise introduced by differential privacy in real-world scenarios, thus reducing the resulting utility degradation, while still holding against the adversary model adhering with the original definition of differential privacy.
We provide a thorough evaluation based on a fully implemented Bes prototype and conducted with real energy consumption data. We show how a large number of events can be aggregated in a private fashion with low processing latency by a single-board device, similar in performance to the devices deployed in Advanced Metering Infrastructures.

References

[1]
G. Ács and C. Castelluccia. I have a DREAM!: Differentially private smart metering. In Proceedings of the 13th International Conference on Information Hiding, IH'11. Springer-Verlag, 2011.
[2]
G. Barthe, G. Danezis, B. Gregoire, C. Kunz, and S. Zanella-Beguelin. Verified computational differential privacy with applications to smart metering. In Computer Security Foundations Symposium (CSF), 2013 IEEE 26th, June 2013.
[3]
J. E. Cabral, J. O. Pinto, and A. M. Pinto. Fraud detection system for high and low voltage electricity consumers based on data mining. In Power & Energy Society General Meeting. PES'09. IEEE, 2009.
[4]
J. Cao, Q. Xiao, G. Ghinita, N. Li, E. Bertino, and K.-L. Tan. Efficient and accurate strategies for differentially-private sliding window queries. Cyber Center Publications, Jan. 2013.
[5]
D. Cederman, V. Gulisano, Y. Nikolakopoulos, M. Papatriantafilou, and P. Tsigas. Concurrent data structures for efficient streaming aggregation. Report, Chalmers University of Technology, 2013.
[6]
T. H. Chan, E. Shi, and D. Song. Private and continual release of statistics. In Automata, Languages and Programming. Springer, 2010.
[7]
W.-Y. Day and N. Li. Differentially private publishing of high-dimensional data using sensitivity control. In Proceedings of the 10th ACM Symposium on Information, Computer and Communications Security, pages 451--462. ACM, 2015.
[8]
C. Dwork, F. McSherry, K. Nissim, and A. Smith. Calibrating noise to sensitivity in private data analysis. In S. Halevi and T. Rabin, editors, Theory of Cryptography, number 3876 in Lecture Notes in Computer Science. Springer Berlin Heidelberg, Jan. 2006.
[9]
C. Dwork, M. Naor, T. Pitassi, and G. N. Rothblum. Differential privacy under continual observation. In Proceedings of the ACM Symposium on Theory of Computing, STOC '10. ACM, 2010.
[10]
M. A. Faisal, Z. Aung, J. R. Williams, and A. Sanchez. Securing advanced metering infrastructure using intrusion detection system with data stream mining. In Intelligence and Security Informatics. Springer, 2012.
[11]
L. Fan and L. Xiong. Real-time aggregate monitoring with differential privacy. In Proceedings of the 21st ACM International Conference on Information and Knowledge Management, CIKM '12. ACM, 2012.
[12]
L. Fan and L. Xiong. An adaptive approach to real-time aggregate monitoring with differential privacy. IEEE Transactions on Knowledge and Data Engineering, 26(9):2094--2106, Sept 2014.
[13]
V. Gulisano, M. Almgren, and M. Papatriantafilou. METIS: a two-tier intrusion detection system for advanced metering infrastructures. In Proceedings of the 5th international conference on Future energy systems. ACM, 2014.
[14]
V. Gulisano, M. Almgren, and M. Papatriantafilou. Online and scalable data validation in advanced metering infrastructures. In The 5th IEEE PES Innovative Smart Grid Technologies (ISGT) European Conference, 2014.
[15]
V. Gulisano, Y. Nikolakopoulos, M. Papatriantafilou, and P. Tsigas. Scalejoin: A deterministic, disjoint-parallel and skew-resilient stream join. In Big Data (Big Data), 2015 IEEE International Conference on, pages 144--153, 2015.
[16]
G. W. Hart. Nonintrusive appliance load monitoring. Proceedings of the IEEE, 80(12):1870--1891, 1992.
[17]
M. Jawurek, F. Kerschbaum, and G. Danezis. Sok: Privacy technologies for smart grids--a survey of options. Microsoft Res., Cambridge, UK, 2012.
[18]
M. Jelasity and K. P. Birman. Distributional differential privacy for large-scale smart metering. In Proceedings of the 2nd ACM workshop on Information hiding and multimedia security, pages 141--146. ACM, 2014.
[19]
G. Kellaris, S. Papadopoulos, X. Xiao, and D. Papadias. Differentially private event sequences over infinite streams. Proceedings of the VLDB Endowment, 7(12), 2014.
[20]
F. McSherry. Privacy integrated queries. In Proceedings of the 2009 ACM SIGMOD International Conference on Management of Data (SIGMOD). Association for Computing Machinery, Inc., June 2009.
[21]
P. Mohan, A. Thakurta, E. Shi, D. Song, and D. Culler. Gupt: Privacy preserving data analysis made easy. In Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data, SIGMOD '12. ACM, 2012.
[22]
Odroid-XU3. http://www.hardkernel.com.
[23]
The World bank. Electric power consumption (kWh per capita). http://data.worldbank.org/indicator/EG.USE.ELEC.KH.PC?page=1.
[24]
V. Rastogi and S. Nath. Differentially private aggregation of distributed time-series with transformation and encryption. In Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data, SIGMOD '10. ACM, 2010.
[25]
E. Shi, T.-H. H. Chan, E. G. Rieffel, R. Chow, and D. Song. Privacy-preserving aggregation of time-series data. In NDSS, volume 2, 2011.
[26]
Storm project. http://storm.apache.org/.
[27]
J. W. Taylor. An evaluation of methods for very short-term load forecasting using minute-by-minute british data. International Journal of Forecasting, 2008.
[28]
V. Tudor, M. Almgren, and M. Papatriantafilou. Analysis of the impact of data granularity on privacy for the smart grid. In Proceedings of the 12th ACM Workshop on Workshop on Privacy in the Electronic Society, WPES '13, pages 61--70, New York, NY, USA, 2013.
[29]
J. Zhao, T. Jung, Y. Wang, and X. Li. Achieving differential privacy of data disclosure in the smart grid. In INFOCOM, 2014 Proceedings IEEE, April 2014.

Cited By

View all
  • (2022)Preserving Privacy of Smart Meter Data in a Smart Grid EnvironmentIEEE Transactions on Industrial Informatics10.1109/TII.2021.307491518:1(707-718)Online publication date: Jan-2022
  • (2020)Towards Differentially Private Truth Discovery for Crowd Sensing Systems2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)10.1109/ICDCS47774.2020.00037(1156-1166)Online publication date: Nov-2020
  • (2019)DRIVEN: a Framework for Efficient Data Retrieval and Clustering in Vehicular Networks2019 IEEE 35th International Conference on Data Engineering (ICDE)10.1109/ICDE.2019.00201(1850-1861)Online publication date: Apr-2019
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
CPSS '16: Proceedings of the 2nd ACM International Workshop on Cyber-Physical System Security
May 2016
102 pages
ISBN:9781450342889
DOI:10.1145/2899015
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 May 2016

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. advanced metering infrastructures
  2. data streaming
  3. differential privacy

Qualifiers

  • Research-article

Conference

ASIA CCS '16
Sponsor:

Acceptance Rates

CPSS '16 Paper Acceptance Rate 8 of 28 submissions, 29%;
Overall Acceptance Rate 43 of 135 submissions, 32%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)4
  • Downloads (Last 6 weeks)0
Reflects downloads up to 20 Jan 2025

Other Metrics

Citations

Cited By

View all
  • (2022)Preserving Privacy of Smart Meter Data in a Smart Grid EnvironmentIEEE Transactions on Industrial Informatics10.1109/TII.2021.307491518:1(707-718)Online publication date: Jan-2022
  • (2020)Towards Differentially Private Truth Discovery for Crowd Sensing Systems2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)10.1109/ICDCS47774.2020.00037(1156-1166)Online publication date: Nov-2020
  • (2019)DRIVEN: a Framework for Efficient Data Retrieval and Clustering in Vehicular Networks2019 IEEE 35th International Conference on Data Engineering (ICDE)10.1109/ICDE.2019.00201(1850-1861)Online publication date: Apr-2019
  • (2018)LoCoVoltProceedings of the 12th ACM International Conference on Distributed and Event-based Systems10.1145/3210284.3210298(171-182)Online publication date: 25-Jun-2018
  • (2018)Privacy Risks in Resource Constrained Smart Micro-Grids2018 32nd International Conference on Advanced Information Networking and Applications Workshops (WAINA)10.1109/WAINA.2018.00139(527-532)Online publication date: May-2018
  • (2018)Inferring Private User Behaviour Based on Information LeakageSmart Micro-Grid Systems Security and Privacy10.1007/978-3-319-91427-5_7(145-159)Online publication date: 28-Aug-2018
  • (2016)Employing Private Data in AMI Applications: Short Term Load Forecasting Using Differentially Private Aggregated Data2016 Intl IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld)10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0076(404-413)Online publication date: Jul-2016
  • (2016)Detecting non-technical energy losses through structural periodic patterns in AMI data2016 IEEE International Conference on Big Data (Big Data)10.1109/BigData.2016.7840967(3121-3130)Online publication date: Dec-2016

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media