Abstract
Fog-assisted mobile crowdsensing (MCS) has been applied to various applications to improve the quality of big data services. As two indispensable services of fog-assisted MCS, privacy protection and flexible access control have attracted widespread attention. Although there are already some cryptographic solutions to address the above concerns, they still have some limitations in the development of mobile crowdsensing, such as lacking anonymous protection and only providing unilateral access control (i.g., who can read). Thus, we propose an anonymous bilateral access control protocol (ABAC) with traceability for secure big data transmission in fog-assisted MCS. By combining the designed access control encryption scheme and an efficient group signature, ABAC not only protects the identity privacy of participants but also achieves access control in terms of reading and writing simultaneously. Security analysis and experimental evaluations demonstrate that ABAC fits the requirements of fog-assisted MCS.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
The GNU multiple precision arithmetic library. https://gmplib.org/
The pairing-based cryptography library. https://crypto.stanford.edu/pbc/
Capponi, A., Fiandrino, C., Kantarci, B., Foschini, L., Kliazovich, D., Bouvry, P.: A survey on mobile crowdsensing systems: challenges, solutions, and opportunities. IEEE Commun. Surv. Tutor. 21(3), 2419–2465 (2019). https://doi.org/10.1109/COMST.2019.2914030
Damgård, I., Haagh, H., Orlandi, C.: Access control encryption: enforcing information flow with cryptography. In: Hirt, M., Smith, A. (eds.) TCC 2016. LNCS, vol. 9986, pp. 547–576. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-53644-5_21
Ganti, R.K., Ye, F., Lei, H.: Mobile crowdsensing: current state and future challenges. IEEE Commun. Mag. 49(11), 32–39 (2011). https://doi.org/10.1109/MCOM.2011.6069707
Gisdakis, S., Giannetsos, T., Papadimitratos, P.: Security, privacy, and incentive provision for mobile crowd sensing systems. IEEE Internet Things J. 3(5), 839–853 (2016). https://doi.org/10.1109/JIOT.2016.2560768
Ho, T.H., Yen, L.H., Tseng, C.C.: Simple-yet-efficient construction and revocation of group signatures. Int. J. Found. Comput. Sci. 26(5), 611–624 (2015). https://doi.org/10.1142/S0129054115500343
Jin, H., Su, L., Xiao, H., Nahrstedt, K.: Inception: incentivizing privacy-preserving data aggregation for mobile crowd sensing systems. In: Proceedings of ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc), pp. 341–350 (2016). https://doi.org/10.1145/2942358.2942375
Li, T., Jung, T., Qiu, Z., Li, H., Cao, L., Wang, Y.: Scalable privacy-preserving participant selection for mobile crowdsensing systems: participant grouping and secure group bidding. IEEE Trans. Netw. Sci. Eng. 7(2), 855–868 (2018). https://doi.org/10.1109/TNSE.2018.2791948
Liu, B., Chen, L., Zhu, X., Zhang, Y., Zhang, C., Qiu, W.: Protecting location privacy in spatial crowdsourcing using encrypted data. In: Proceedings of International Conference on Extending Database Technology (EDBT) (2017). https://doi.org/10.5441/002/edbt.2017.49
Liu, J., Shen, H., Narman, H.S., Chung, W., Lin, Z.: A survey of mobile crowdsensing techniques: a critical component for the internet of things. ACM Trans. Cyber-Physical Syst. 2(3), 1–26 (2018). https://doi.org/10.1145/3185504
Marjanović, M., Grubeša, S., Žarko, I.P.: Air and noise pollution monitoring in the city of Zagreb by using mobile crowdsensing. In: Proceedings of International Conference on Software, Telecommunications and Computer Networks (SoftCOM), pp. 1–5 (2017). https://doi.org/10.23919/SOFTCOM.2017.8115502
Miao, C., et al.: Cloud-enabled privacy-preserving truth discovery in crowd sensing systems. In: Proceedings of ACM Conference on Embedded Networked Sensor Systems (SenSys), pp. 183–196 (2015). https://doi.org/10.1145/2809695.2809719
Miao, C., Su, L., Jiang, W., Li, Y., Tian, M.: A lightweight privacy-preserving truth discovery framework for mobile crowd sensing systems. In: Proceedings of IEEE Conference on Computer Communications (INFOCOM) (2017). https://doi.org/10.1109/INFOCOM.2017.8057114
Ni, J., Zhang, A., Lin, X., Shen, X.S.: Security, privacy, and fairness in fog-based vehicular crowdsensing. IEEE Commun. Mag. 55(6), 146–152 (2017). https://doi.org/10.1109/MCOM.2017.1600679
Ni, J., Zhang, K., Yu, Y., Lin, X., Shen, X.S.: Providing task allocation and secure deduplication for mobile crowdsensing via fog computing. IEEE Trans. Dependable Secure Comput. 17(3), 581–594 (2018). https://doi.org/10.1109/TDSC.2018.2791432
Wang, J., Wang, Y., Zhang, D., Helal, S.: Energy saving techniques in mobile crowd sensing: current state and future opportunities. IEEE Commun. Mag. 56(5), 164–169 (2018). https://doi.org/10.1109/MCOM.2018.1700644
Xiao, M., Wu, J., Zhang, S., Yu, J.: Secret-sharing-based secure user recruitment protocol for mobile crowdsensing. In: Proceedings of IEEE Conference on Computer Communications (INFOCOM) (2017). https://doi.org/10.1109/INFOCOM.2017.8057032
Xu, G., Li, H., Tan, C., Liu, D., Dai, Y., Yang, K.: Achieving efficient and privacy-preserving truth discovery in crowd sensing systems. Comput. Secur. 69, 114–126 (2017). https://doi.org/10.1016/j.cose.2016.11.014
Ye, D., Mei, Y., Shang, Y., Zhu, J., Ouyang, K.: Mobile crowd-sensing context aware based fine-grained access control mode. Multimedia Tools Appl. 75(21), 13977–13993 (2015). https://doi.org/10.1007/s11042-015-2693-3
Zheng, Y., Duan, H., Wang, C.: Learning the truth privately and confidently: encrypted confidence-aware truth discovery in mobile crowdsensing. IEEE Trans. Inf. Forensics Secur. 13(10), 2475–2489 (2018). https://doi.org/10.1109/TIFS.2018.2819134
Zheng, Y., Duan, H., Yuan, X., Wang, C.: Privacy-aware and efficient mobile crowdsensing with truth discovery. IEEE Trans. Dependable Secure Comput. 17(1), 121–133 (2017). https://doi.org/10.1109/TDSC.2017.2753245
Acknowledgment
This work was supported by the National Natural Science Foundation of China (Nos. U20A20176 and 62072062), the Natural Science Foundation of Chongqing, China (No. cstc2019jcyjjqX0026), and the Guangxi Key Laboratory of Trusted Software (No. KX202043).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Chen, B., Wang, Z., Xiang, T., Yang, L., Yan, H., Li, J. (2021). ABAC: Anonymous Bilateral Access Control Protocol with Traceability for Fog-Assisted Mobile Crowdsensing. In: Tan, Y., Shi, Y., Zomaya, A., Yan, H., Cai, J. (eds) Data Mining and Big Data. DMBD 2021. Communications in Computer and Information Science, vol 1454. Springer, Singapore. https://doi.org/10.1007/978-981-16-7502-7_40
Download citation
DOI: https://doi.org/10.1007/978-981-16-7502-7_40
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-7501-0
Online ISBN: 978-981-16-7502-7
eBook Packages: Computer ScienceComputer Science (R0)