Abstract
One problem with the Internet of Things (IoT) is that user data and identities could be used in ways that aren’t what they were meant to be used for. Researchers have come up with different ways to lower privacy risks. But most of the existing solutions still have problems. They also have heavy cryptosystems and policies that are applied on both sensor devices and in the cloud. To solve these privacy problems, fog computing has been added to the edges of IoT networks to help with low latency, computation, and storage. This research uses deep learning models and hashing techniques that give the properties of User validation, Data confidentiality, Data verifiability, and Data integrity, and Data obfuscation that are done using MQTT protocol which derives more confidentiality, making data obscure. The future scope will be applied to AI models which can optimise the setup time for learning and training classification models.
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References
Srinivas J, Das AK, Kumar N, Rodrigues JJPC (2020) Cloud centric authentication for wearable healthcare monitoring system. IEEE Trans Dependable Secure Comput 17(5):942–956
Torkura KA, Sukmana MIH, Cheng F, Meinel C (2020) CloudStrike: chaos engineering for security and resiliency in cloud infrastructure. IEEE Access 8:123044–123060
Majumdar S et al (2022) ProSAS: Proactive security auditing system for clouds. IEEE Trans Dependable Secure Comput 19(4):2517–2534
Yang C, Liu Y, Tao X, Zhao F (2020) Publicly verifiable and efficient fine-grained data deletion scheme in cloud computing. IEEE Access 8:99393–99403
Zhang K, Jiang Z, Ning J, Huang X (2022) Subversion-resistant and consistent attribute-based keyword search for secure cloud storage. IEEE Trans Inf Forensics Secur 17:1771–1784
Nhlabatsi A et al (2021) Threat-specific security risk evaluation in the cloud. IEEE Trans Cloud Comput 9(2):793–806
An S, Leung A, Hong JB, Eom T, Park JS (2022) Toward automated security analysis and enforcement for cloud computing using graphical models for security. IEEE Access 10:75117–75134
Halabi T, Bellaiche M (2018) Towards security-based formation of cloud federations: A game theoretical approach. IEEE Trans Cloud Comput 8(3):1–1
Albouq SS, Sen AAA, Namoun A, Bahbouh NM, Alkhodre AB, Alshanqiti A (2020) A double obfuscation approach for protecting the privacy of IoT location based applications. IEEE Access 8:129415–129431
Mishra P, Varadharajan V, Pilli E, Tupakula U (2018) VMGuard: a VMI-based security architecture for intrusion detection in cloud environment. IEEE Trans Cloud Comput 8(3):1–1
Deng R, Lu R, Lai C, Luan TH, Liang H (2016) Optimal workload allocation in fog-cloud computing towards balanced delay and power consumption. IEEE Internet Things J 3(6):1–1
Hu P, Ning H, Qiu T, Song H, Wang Y, Yao X (2017) Security and privacy preservation scheme of face identification and resolution framework using fog computing in internet of things. IEEE Internet Things J 4(5):1143–1155
Jiang Y, Susilo W, Mu Y, Guo F (2016) Ciphertext-policy attribute-based encryption with key-delegation abuse resistance. In: Information security and privacy. Springer International Publishing, Cham, pp 477–494
Huang Q, Wang L, Yang Y (2018) DECENT: Secure and fine-grained data access control with policy updating for constrained IoT devices. World Wide Web 21(1):151–167
Alrawais A, Alhothaily A, Hu C, Xing X, Cheng X (2017) An attribute-based encryption scheme to secure fog communications. IEEE Access 5:9131–9138
Lu R, Heung K, Lashkari AH, Ghorbani AA (2017) A lightweight privacy-preserving data aggregation scheme for fog computing-enhanced IoT. IEEE Access 5:3302–3312
Kaur J, Agrawal A, Khan RA (2022) Encryfuscation: a model for preserving data and location privacy in fog based IoT scenario. J King Saud Univ - Comput Inf Sci 34(9):6808–6817
Chen X, Mu Y (2016) Preserving user location privacy for location-based service. In: Green, pervasive, and cloud computing. Springer International Publishing, Cham, pp 290–300
Fischer M, Kumper D, Tonjes R (2019) Towards improving the privacy in the MQTT protocol. In: 2019 global IoT summit (GIoTS), pp 1–6
Kumar RA, Vinuthna K (2021) Randomized ensemble SVM based deep learning with verifiable dynamic access control using user revocation in IoT architecture. Sadhana 46(4)
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Kumar, R.A., Rekha, G., Vinuthna, K. (2023). A Novel Approach for Privacy Preserving Technique in IoT Fog and Cloud Environment. In: Raj, J.S., Perikos, I., Balas, V.E. (eds) Intelligent Sustainable Systems. ICoISS 2023. Lecture Notes in Networks and Systems, vol 665. Springer, Singapore. https://doi.org/10.1007/978-981-99-1726-6_10
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DOI: https://doi.org/10.1007/978-981-99-1726-6_10
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