Achieving Differential Privacy in Smart Home Scenarios
Resumo
With the growth of the Internet of Things (IoT) and Smart Homes, there is an ever-growing amount of data coming from within people’s houses. These data are intrinsically private and should be treated carefully, despite their high value for analysis. In this work, we propose a differentially private strategy to estimate frequencies of values in the context of Smart Home data.
Palavras-chave:
Privacy, Differential Privacy, Smart Home
Referências
Ács, G. and Castelluccia, C. (2011). I have a dream! (differentially private smart metering). In International Workshop on Information Hiding, pages 118–132. Springer. DOI: https://doi.org/10.1007/978-3-642-24178-9_9
Cao, Y. and Yoshikawa, M. (2015). Differentially private real-time data release over infinite trajectory streams. In 2015 16th IEEE International Conference on Mobile Data Management, volume 2, pages 68–73. IEEE. DOI: https://doi.org/10.1109/MDM.2015.15
Dwork, C., McSherry, F., Nissim, K., and Smith, A. (2006). Calibrating noise to sensitivity in private data analysis. In Theory of cryptography conference, pages 265–284. Springer. DOI: https://doi.org/10.1007/11681878_14
Dwork, C. and Roth, A. (2014). The algorithmic foundations of differential privacy. Found. Trends Theor. Comput. Sci., 9(3–4):211–407.DOI: http://dx.doi.org/10.1561/0400000042
Erlingsson, U., Pihur, V., and Korolova, A. (2014). Rappor: Randomized aggregatable privacy-preserving ordinal response. In Proceedings of the 2014 ACM SIGSAC conference on computer and communications security, pages 1054–1067. ACM. DOI: https://doi.org/10.1145/2660267.2660348
Leal, B. C., Vidal, I. C., Brito, F. T., Nobre, J. S., and Machado, J. C. (2018). doca: Achieving privacy in data streams. In Data Privacy Management, Cryptocurrencies and Blockchain Technology, pages 279–295. Springer. DOI: https://doi.org/10.1007/978-3-030-00305-0_20
McSherry, F. D. (2009). Privacy integrated queries: an extensible platform for privacypreserving data analysis. In Proceedings of the 2009 ACM SIGMOD International Conference on Management of data, pages 19–30. ACM. DOI: https://doi.org/10.1145/1559845.1559850
Molina-Markham, A., Shenoy, P., Fu, K., Cecchet, E., and Irwin, D. (2010). Private memoirs of a smart meter. In Proceedings of the 2nd ACM workshop on embedded sensing systems for energy-efficiency in building, pages 61–66. ACM. DOI: https://doi.org/10.1145/1878431.1878446
Shi, W., Cao, J., Zhang, Q., Li, Y., and Xu, L. (2016). Edge computing: Vision and challenges. IEEE Internet of Things Journal, 3(5):637–646. DOI: https://doi.org/10.1109/JIOT.2016.2579198
UK Power Networks (2015). SmartMeter Energy Consumption Data in London Households. https://data.london.gov.uk/dataset/ smartmeter-energy-use-data-in-london-households. Accessed: 2019-06-28.
Wang, T., Blocki, J., Li, N., and Jha, S. (2017). Locally differentially private protocols for frequency estimation. In 26th {USENIX} Security Symposium ({USENIX} Security 17), pages 729–745.
Cao, Y. and Yoshikawa, M. (2015). Differentially private real-time data release over infinite trajectory streams. In 2015 16th IEEE International Conference on Mobile Data Management, volume 2, pages 68–73. IEEE. DOI: https://doi.org/10.1109/MDM.2015.15
Dwork, C., McSherry, F., Nissim, K., and Smith, A. (2006). Calibrating noise to sensitivity in private data analysis. In Theory of cryptography conference, pages 265–284. Springer. DOI: https://doi.org/10.1007/11681878_14
Dwork, C. and Roth, A. (2014). The algorithmic foundations of differential privacy. Found. Trends Theor. Comput. Sci., 9(3–4):211–407.DOI: http://dx.doi.org/10.1561/0400000042
Erlingsson, U., Pihur, V., and Korolova, A. (2014). Rappor: Randomized aggregatable privacy-preserving ordinal response. In Proceedings of the 2014 ACM SIGSAC conference on computer and communications security, pages 1054–1067. ACM. DOI: https://doi.org/10.1145/2660267.2660348
Leal, B. C., Vidal, I. C., Brito, F. T., Nobre, J. S., and Machado, J. C. (2018). doca: Achieving privacy in data streams. In Data Privacy Management, Cryptocurrencies and Blockchain Technology, pages 279–295. Springer. DOI: https://doi.org/10.1007/978-3-030-00305-0_20
McSherry, F. D. (2009). Privacy integrated queries: an extensible platform for privacypreserving data analysis. In Proceedings of the 2009 ACM SIGMOD International Conference on Management of data, pages 19–30. ACM. DOI: https://doi.org/10.1145/1559845.1559850
Molina-Markham, A., Shenoy, P., Fu, K., Cecchet, E., and Irwin, D. (2010). Private memoirs of a smart meter. In Proceedings of the 2nd ACM workshop on embedded sensing systems for energy-efficiency in building, pages 61–66. ACM. DOI: https://doi.org/10.1145/1878431.1878446
Shi, W., Cao, J., Zhang, Q., Li, Y., and Xu, L. (2016). Edge computing: Vision and challenges. IEEE Internet of Things Journal, 3(5):637–646. DOI: https://doi.org/10.1109/JIOT.2016.2579198
UK Power Networks (2015). SmartMeter Energy Consumption Data in London Households. https://data.london.gov.uk/dataset/ smartmeter-energy-use-data-in-london-households. Accessed: 2019-06-28.
Wang, T., Blocki, J., Li, N., and Jha, S. (2017). Locally differentially private protocols for frequency estimation. In 26th {USENIX} Security Symposium ({USENIX} Security 17), pages 729–745.
Publicado
07/10/2019
Como Citar
VIDAL, Israel C.; ROUSSEAU, Franck; MACHADO, Javam C..
Achieving Differential Privacy in Smart Home Scenarios. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 34. , 2019, Fortaleza.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 211-216.
ISSN 2763-8979.
DOI: https://doi.org/10.5753/sbbd.2019.8825.
