Abstract
The popularity of smart home has added a lot of convenience to people’s lives. However, while users use these smart products, users’ privacy data has also been leaked and it may cause some risks. Besides, because of untrusted third-party servers, we simply use traditional privacy-preserving methods could no longer protect users’ private information effectively. In order to solve these problems, this paper proposes a privacy-preserving method for multi-private data: We first determine the privacy data format that needs to be protected, such as audio or text. Secondly, if the data format is text, we will use the local differential privacy method. We first obtain the key attributes of the user from the key information chain, and then select the appropriate localized differential privacy method according to the text characteristics of the key attributes. The user realizes the local disturbance of the data and then uploads it to the data collection center– the cloud platform. Finally, when an attacker attempts to obtain user information from the cloud platform, it uses the central differential privacy method to add noise and the noise-added data is transmitted to the attacker. If the data format is voice frequency, we first convert the voice information into binary code, then chaotically encrypt the binary code, and upload the encrypted binary code to the cloud platform. We verify the effectiveness of our methods by experiments, and it can protect users’ privacy information better.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ronen, E., Shamir, A., Weingarten, A.-O., O’flynn, C.: IoT goes nuclear: creating a ZigBee chain reaction. In: IEEE Security and Privacy (SP) (2017)
Fernandes, E., Jung, J., Prakash, A.: Security analysis of emerging smart home applications. In: IEEE Security and Privacy (SP) (2016)
Celik, Z.B., Mcdaniel, P., Tan, G.: SOTERIA: automated IoT safety and security analysis. In: USENIX ATC (2018)
Fernandes, E., Paupore, J., Rahmati, A., Simionato, D., Conti, M., Prakash, A.: FlowFence: practical data protection for emerging IoT application frameworks. In: USENIX Security (2016)
Jia, Y.J., et al.: ContexIoT: towards providing contextual integrity to appified IoT platforms. In: NDSS (2017)
Zawoad, S., Hasan, R.: FAIoT: towards building a forensics aware eco system for the internet of things. In: SCC, pp. 279–284 (2015)
Wang, Q., Hassan, W.U., Bates, A., Gunter, C.: Fear and logging in the internet of things. In: NDSS (2018)
Celik, Z.B., et al.: Sensitive information tracking in commodity IoT. In: USENIX Security (2018)
Dwork, C., Lei, J.: Differential privacy and robust statistics. In: Proceedings of the 41st Annual ACM Symposium on Theory of Computing, pp. 371–380. ACM (2009). https://doi.org/10.1145/1536414.1536466
Smith, A.: Privacy-preserving statistical estimation with optimal convergence rates. In: Proceedings of the 43rd Annual ACM Symposium on Theory of Computing, pp. 813–822. ACM (2011). https://doi.org/10.1145/1993636.1993743
Kasiviswanathan, S.P., Lee, H.K., Nissim, K., Raskhodnikova, S., Smith, A.: What can we learn privately. In: Proceedings of the 49th Annual IEEE Symposium on Foundations of Computer Science (FOCS), pp. 531–540. IEEE (2008)
Duchi, J.C., Jordan, M.I., Wainwright, M.J.: Local privacy and statistical minimax rates. In: Proceedings of the 54th Annual IEEE Symposium on Foundations of Computer Science (FOCS), pp. 429–438. IEEE (2013). https://doi.org/10.1109/focs.2013.53
Finn, R.L., Wright, D., Friedewald, M.: Seven types of privacy. In: Gutwirth, S., Leenes, R., de Hert, P., Poullet, Y. (eds.) European Data Protection: Coming of Age, pp. 3–32. Springer, Dordrecht (2013). https://doi.org/10.1007/978-94-007-5170-5_1
Qin, Z., Yan, J., Ren, K., Chen, C.W., Wang, C.: Towards efficient privacy-preserving image feature extraction in cloud computing. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 497–506. ACM (2014). https://doi.org/10.1145/2647868.2654941
Ren, K.: Privacy-preserving image processing in cloud computing. Chin. J. Netw. Inf. Secur. 1, 12–17 (2016)
Erlingsson, Ú., Pihur, V., Korolova, A.: RAPPOR: randomized aggregatable privacy-preserving ordinal response. In: Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security, pp. 1054–1067. ACM (2014). https://doi.org/10.1145/2660267.2660348
Bloom, B.H.: Space/time trade-offs in hash coding with allowable errors. Commun. ACM 13(7), 422–426 (1970). https://doi.org/10.1145/362686.362692
Tibshiran, R.: Regression shrinkage and selection via the Lasso. J. Roy. Stat. Soc. (Ser. B-Methodol.) 58, 267–288 (1996)
Bassily, R., Smith, A.: Local, private, efficient protocols for succinct histograms. In: Proceedings of the 47th Annual ACM on Symposium on Theory of Computing, pp. 127–135. ACM (2015). https://doi.org/10.1145/2746539.2746632
Kairouz, P., Oh, S., Viswanath, P.: Extremal mechanisms for local differential privacy. In: Advances in Neural Information Processing Systems, pp. 2879–2887 (2014)
Kairouz, P., Bonawitz, K., Ramage, D.: Discrete distribution estimation under local privacy. In: Proceedings of the 33rd International Conference on Machine Learning, New York, pp. 2436–2444 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Wang, H., Feng, T., Ren, Z., Gao, L., Zheng, J. (2020). Towards Efficient Privacy-Preserving Personal Information in User Daily Life. In: Li, B., Zheng, J., Fang, Y., Yang, M., Yan, Z. (eds) IoT as a Service. IoTaaS 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 316. Springer, Cham. https://doi.org/10.1007/978-3-030-44751-9_42
Download citation
DOI: https://doi.org/10.1007/978-3-030-44751-9_42
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-44750-2
Online ISBN: 978-3-030-44751-9
eBook Packages: Computer ScienceComputer Science (R0)