survey

A Survey on Privacy Preservation in Fog-Enabled Internet of Things

Authors:
Kinza Sarwar

School of Engineering, Computer & Mathematical Sciences, Auckland University of Technology, Auckland, New Zealand

School of Engineering, Computer & Mathematical Sciences, Auckland University of Technology, Auckland, New Zealand
View Profile

,
Sira Yongchareon

School of Engineering, Computer & Mathematical Sciences, Auckland University of Technology, Auckland, New Zealand

School of Engineering, Computer & Mathematical Sciences, Auckland University of Technology, Auckland, New Zealand
View Profile

,
Jian Yu

School of Engineering, Computer & Mathematical Sciences, Auckland University of Technology, Auckland, New Zealand

School of Engineering, Computer & Mathematical Sciences, Auckland University of Technology, Auckland, New Zealand
View Profile

,
Saeed Ur Rehman

College of Science and Engineering, Flinders University, Adelaide, Australia

College of Science and Engineering, Flinders University, Adelaide, Australia
View Profile

Authors Info & Claims

ACM Computing Surveys Volume 55 Issue 1Article No.: 2pp 1–39https://doi.org/10.1145/3474554

Published:23 November 2021Publication History

ACM Computing Surveys

Abstract

Despite the rapid growth and advancement in the Internet of Things (IoT), there are critical challenges that need to be addressed before the full adoption of the IoT. Data privacy is one of the hurdles towards the adoption of IoT as there might be potential misuse of users’ data and their identity in IoT applications. Several researchers have proposed different approaches to reduce privacy risks. However, most of the existing solutions still suffer from various drawbacks, such as huge bandwidth utilization and network latency, heavyweight cryptosystems, and policies that are applied on sensor devices and in the cloud. To address these issues, fog computing has been introduced for IoT network edges providing low latency, computation, and storage services. In this survey, we comprehensively review and classify privacy requirements for an in-depth understanding of privacy implications in IoT applications. Based on the classification, we highlight ongoing research efforts and limitations of the existing privacy-preservation techniques and map the existing IoT schemes with Fog-enabled IoT schemes to elaborate on the benefits and improvements that Fog-enabled IoT can bring to preserve data privacy in IoT applications. Lastly, we enumerate key research challenges and point out future research directions.

REFERENCES

[1] Yang Y. et al. 2017. A survey on security and privacy issues in Internet-of-Things. IEEE Internet of Things Journal 4, 5 (2017), 1250–1258.Google ScholarCross Ref
[2] Connected IoT Devices Forecast. Help Net Security 2019 [cited 2019; 41.6 billion IoT devices will be generating 79.4 zettabytes of data in 2025]. Available from https://www.helpnetsecurity.com/2019/06/21/connected-iot-devices-forecast/.Google Scholar
[3] Bellendorf J. and Mann Z. Á.. 2020. Classification of optimization problems in Fog computing. Future Generation Computer Systems 107 (2020), 158–176.Google ScholarDigital Library
[4] Bonomi F. et al. 2012. Fog computing and its role in the Internet of Things. In Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing. 2012. ACM. Google ScholarDigital Library
[5] Lu R. et al. 2017. A lightweight privacy-preserving data aggregation scheme for Fog computing-enhanced IoT. IEEE Access 5 (2017), 3302--3312.Google ScholarCross Ref
[6] Huang Q., Yang Y., and Wang L.. 2017. Secure data access control with ciphertext update and computation outsourcing in Fog computing for Internet of Things. IEEE Access 5 (2017). 12941–12950.Google ScholarCross Ref
[7] Stergiou C., K. E. Psannis, B. G. Kim, and B. Gupta. 2018. Secure integration of IoT and Cloud computing. Future Generation Computer Systems 78 (2018), 964–975.Google ScholarCross Ref
[8] Adat V. and Gupta B. J. T. S.. 2018. Security in Internet of Things: Issues, challenges, taxonomy, and architecture. Telecommunication Systems 67, 3 (2018), 423–441.Google ScholarCross Ref
[9] Hossain M. M., Fotouhi M., and Hasan R.. 2015. Towards an analysis of security issues, challenges, and open problems in the Internet of Things. In 2015 IEEE World Congress on Services. 2015. IEEE. Google ScholarDigital Library
[10] Nepal S., Ranjan R., and Choo K.-K. R. J. I. C. C.. 2015. Trustworthy processing of healthcare big data in hybrid clouds. IEEE Cloud Computing 2, 2 (2015), 78–84.Google ScholarCross Ref
[11] Haghighat M., Zonouz S., and Abdel-Mottaleb M. J. E. S. W. A.. 2015. CloudID: Trustworthy cloud-based and cross-enterprise biometric identification. Expert Systems with Applications 42, 21 (2015), 7905–7916. Google ScholarDigital Library
[12] Pandit K. et al. 2013. Adaptive traffic signal control with vehicular ad hoc networks. IEEE Transactions on Vehicular Technology 62, 4 (2013), 1459–1471.Google ScholarCross Ref
[13] Zhang L. et al. 2017. Distributed aggregate privacy-preserving authentication in VANETs. IEEE Transactions on Intelligent Transportation Systems 18, 3 (2017), 516–526. Google ScholarDigital Library
[14] Xia Z. et al. 2016. A privacy-preserving and copy-deterrence content-based image retrieval scheme in Cloud computing. IEEE Transactions on Information Forensics and Security 11, 11 (2016), 2594–2608. Google ScholarDigital Library
[15] Yuan J. and Yu S.. 2013. Efficient privacy-preserving biometric identification in Cloud computing. In INFOCOM, 2013 Proceedings IEEE. 2013. IEEE.Google ScholarCross Ref
[16] Yang C. et al. 2017. Towards product customization and personalization in IoT-enabled Cloud manufacturing. Cluster Computing 20, 2 (2017), 1717–1730. Google ScholarDigital Library
[17] Yang M. et al. 2018. Machine learning differential privacy with multifunctional aggregation in a Fog computing architecture. IEEE Access 2018.Google Scholar
[18] Fernández-Alemán J. L. et al. 2013. Security and privacy in electronic health records: A systematic literature review. Journal of Biomedical Informatics 46, 3 (2013), 541–562.Google ScholarCross Ref
[19] Zhou J. et al. 2017. Security and privacy for Cloud-based IoT: Challenges. IEEE Communications Magazine 55, 1 (2017), 26–33. Google ScholarDigital Library
[20] Lopez J. et al. 2017. Evolving privacy: From sensors to the Internet of Things. Future Generation Computer Systems 75 (2017), 46–57.Google ScholarCross Ref
[21] Sarwar K., Yongchareon S., and Yu J.. 2018. A brief survey on IoT privacy: Taxonomy, issues and future trends. In International Conference on Service-Oriented Computing. 2018. Springer.Google Scholar
[22] Celik Z. B. et al. 2019. Program analysis of commodity IoT applications for security and privacy: Challenges and opportunities. ACM Computing Surveys (CSUR) 52, 4 (2019), 74. Google ScholarDigital Library
[23] Aleisa N. and Renaud K.. 2017. Privacy of the Internet of Things: A systematic literature review. 2017.Google Scholar
[24] Tewari A. and Gupta B. J. F. G. C. S.. 2018. Security, privacy and trust of different layers in Internet-of-Things (IoTs) framework. 2018.Google Scholar
[25] Mukherjee M. et al. 2017. Security and privacy in Fog computing: Challenges. IEEE Access 5 (2017), 19293–19304.Google ScholarCross Ref
[26] Liu X. et al. 2018. Security and privacy challenges for Internet-of-Things and Fog computing. Wireless Communications and Mobile Computing, 2018.Google ScholarDigital Library
[27] Yu D. et al. 2018. A survey on security issues in services communication of microservices-enabled Fog applications. Concurrency and Computation: Practice and Experience 2018: e4436.Google Scholar
[28] Zhang P., Zhou M., and Fortino G.. 2018. Security and trust issues in Fog computing: A survey. Future Generation Computer Systems 88 (2018), 16–27.Google ScholarDigital Library
[29] Stojmenovic I. and Wen S.. 2014. The Fog computing paradigm: Scenarios and security issues. In 2014 Federated Conference on Computer Science and Information Systems. 2014. IEEE.Google Scholar
[30] L. Bai, Y. Liu, X. Wang, N. Patterson, and F. Jiang. 2019. A survey on cryptographic security and information hiding technology for Cloud or Fog-based IoT system. Journal of Information Hiding and Privacy Protection 1, 1 (2019), p.1.Google Scholar
[31] Broenink G. et al. 2010. The privacy coach: Supporting customer privacy in the Internet of Things. arXiv preprint arXiv:1001.4459, 2010.Google Scholar
[32] Hu C., Zhang J., and Wen Q.. 2011. An identity-based personal location system with protected privacy in IoT. In 2011 4th IEEE International Conference on Broadband Network and Multimedia Technology. 2011. IEEE.Google ScholarCross Ref
[33] Jhumka A., Leeke M., and Shrestha S.. 2011. On the use of fake sources for source location privacy: Trade-offs between energy and privacy. The Computer Journal 54, 6 (2011), 860–874. Google ScholarDigital Library
[34] Raij A. et al. 2011. Privacy risks emerging from the adoption of innocuous wearable sensors in the mobile environment. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 2011. ACM. Google ScholarDigital Library
[35] Chen C. M. et al. 2012. RCDA: Recoverable concealed data aggregation for data integrity in wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems 23, 4 (2012), 727–734. Google ScholarDigital Library
[36] Wei W., Xu F., and Li Q.. 2012. Mobishare: Flexible privacy-preserving location sharing in mobile online social networks. In 2012 Proceedings IEEE INFOCOM. 2012. IEEE.Google ScholarCross Ref
[37] Elaluf-Calderwood S. and Liebenau J.. 2012. Privacy, identity and security concerns: Enterprise strategic decision making and business model development for mobile payments in NFC. 2012.Google Scholar
[38] Alcaide A. et al. 2013. Anonymous authentication for privacy-preserving IoT target-driven applications. Computers & Security 37 (2013), 111–123. Google ScholarDigital Library
[39] Poslad S., Hamdi M., and Abie H.. 2013. Adaptive security and privacy management for the Internet of Things (ASPI 2013). In Proceedings of the 2013 ACM Conference on Pervasive and Ubiquitous Computing Adjunct Publication. 2013. Google ScholarDigital Library
[40] Virkki J. and Chen L.. 2013. Personal perspectives: Individual privacy in the IoT. 2013.Google Scholar
[41] Kai K., Pang Z., and Cong W.. 2013. Security and privacy mechanism for health Internet of Things. The Journal of China Universities of Posts and Telecommunications 20 (2013), p. 64–68.Google ScholarCross Ref
[42] Yao L. et al. 2013. Protecting the sink location privacy in wireless sensor networks. Personal and Ubiquitous Computing 17, 5 (2013), 883–893. Google ScholarDigital Library
[43] J. Su, D. Cao, B. Zhao, X. Wang, and I. You. 2014. ePASS: An expressive attribute-based signature scheme with privacy and an unforgeability guarantee for the Internet of Things 33 (2014), 11–18. Google ScholarDigital Library
[44] Denning T., Dehlawi Z., and Kohno T.. 2014. In situ with bystanders of augmented reality glasses: Perspectives on recording and privacy-mediating technologies. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 2014. ACM. Google ScholarDigital Library
[45] Bonetto M. et al. 2015. Privacy in mini-drone based video surveillance. In Automatic Face and Gesture Recognition (FG), 2015 11th IEEE International Conference and Workshops on. 2015. IEEE.Google ScholarCross Ref
[46] Zöscher L. et al. 2015. Concept for a security aware automatic fare collection system using HF/UHF dual band RFID transponders. In Solid State Device Research Conference (ESSDERC), 2015 45th European. 2015. IEEE.Google ScholarCross Ref
[47] Salonikias S., Mavridis I., and Gritzalis D.. 2015. Access control issues in utilizing Fog computing for transport infrastructure. In International Conference on Critical Information Infrastructures Security. 2015. Springer.Google Scholar
[48] Samani A., Ghenniwa H. H., and Wahaishi A.. 2015. Privacy in Internet of Things: A model and protection framework. Procedia Computer Science 52 (2015), 606–613.Google ScholarDigital Library
[49] Aditya P. et al. 2016. I-pic: A platform for privacy-compliant image capture. In Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services. 2016. ACM. Google ScholarDigital Library
[50] Othman S. B. et al. 2015. Confidentiality and integrity for data aggregation in WSN using homomorphic encryption. Wireless Personal Communications 80, 2 (2015), 867–889. Google ScholarDigital Library
[51] Yang X. et al. 2015. A novel temporal perturbation based privacy-preserving scheme for real-time monitoring systems. Computer Networks 88 (2015), 72–88. Google ScholarDigital Library
[52] Neisse R. et al. 2015. SecKit: A model-based security toolkit for the Internet of Things 54 (2015), 60–76. Google ScholarDigital Library
[53] Seo S.-H., Won J., and Bertino E.. 2016. pCLSC-TKEM: A pairing-free certificateless signcryption-tag key encapsulation mechanism for a privacy-preserving IoT 9, 2 (2016), 101–130. Google ScholarDigital Library
[54] Ghugal S. and Vaidya M.. 2016. Military network security for data retrieval with touch of smell technology. 2016.Google Scholar
[55] Ivaşcu T., Frîncu M., and Negru V.. 2016. Considerations towards security and privacy in Internet of Things based ehealth applications. In 2016 IEEE 14th International Symposium on Intelligent Systems and Informatics (SISY). 2016. IEEE.Google ScholarCross Ref
[56] Y. Mao, J. Li, M. R. Chen, J. Liu, C. Xie, and Y. Zhan. 2016. Fully secure fuzzy identity-based encryption for secure IoT communications 44 (2016), 117–121. Google ScholarDigital Library
[57] Laikin J. F. et al. 2016. Towards fake sources for source location privacy in wireless sensor networks with multiple sources. In 2016 IEEE International Conference on Communication Systems (ICCS). 2016. IEEE.Google ScholarCross Ref
[58] Bradbury M. and Jhumka A.. 2017. Understanding source location privacy protocols in sensor networks via perturbation of time series. In IEEE INFOCOM 2017-IEEE Conference on Computer Communications. 2017. IEEE. Google ScholarDigital Library
[59] Diyanat A., Khonsari A., and Shafiei H.. 2017. Preservation of temporal privacy in body sensor networks. Journal of Network and Computer Applications 96 (2017), 62–71. Google ScholarDigital Library
[60] Wang N. and Zeng J.. 2017. All-direction random routing for source-location privacy protecting against parasitic sensor networks. Sensors 17, 3 (2017), 614.Google ScholarCross Ref
[61] Hu P. et al. 2017. Security and privacy preservation scheme of face identification and resolution framework using Fog computing in Internet of Things. IEEE Internet of Things Journal 4, 5 (2017), 1143–1155.Google ScholarCross Ref
[62] Apthorpe N. et al. 2017. Spying on the smart home: Privacy attacks and defenses on encrypted IoT traffic. 2017.Google Scholar
[63] Wu Q. et al. 2016. Privacy-aware multipath video caching for content-centric networks. 34, 8 (2016), 2219–2230. Google ScholarDigital Library
[64] Kumar P. et al. 2017. Anonymous secure framework in connected smart home environments. IEEE Trans. Information Forensics and Security 12, 4 (2017), 968–979.Google ScholarCross Ref
[65] Amin R., S. H. Islam, G. P. Biswas, M. K. Khan, and N. Kumar. 2018. A robust and anonymous patient monitoring system using wireless medical sensor networks. 80 (2018), 483–495. Google ScholarDigital Library
[66] Kumar P. et al. 2017. Anonymous secure framework in connected smart home environments. IEEE Transactions on Information Forensics and Security 12, 4 (2017), 968–979.Google ScholarCross Ref
[67] Hong Y., Liu W. M., and Wang L.. 2017. Privacy preserving smart meter streaming against information leakage of appliance status. IEEE Transactions on Information Forensics and Security 12, 9 (2017), 2227–2241.Google ScholarDigital Library
[68] Camillo G. L., C. M. Westphall, J. Werner, and C. B. Westphall. 2017. Preserving privacy with fine-grained authorization in an identity management system. 2017: 86.Google Scholar
[69] Dorri A. et al. 2017. Blockchain for IoT security and privacy: The case study of a smart home. In Pervasive Computing and Communications Workshops (PerCom Workshops), 2017 IEEE International Conference on. 2017. IEEE.Google Scholar
[70] Gope P., Lee J., and Quek T. Q.. 2017. Resilience of DoS attacks in designing anonymous user authentication protocol for wireless sensor networks. IEEE Sensors Journal 17, 2 (2017), 498–503.Google ScholarCross Ref
[71] Jayaraman P. P., X. Yang, A. Yavari, D. Georgakopoulos, and X. Yi. 2017. Privacy preserving Internet of Things: From privacy techniques to a blueprint architecture and efficient implementation. 76 (2017), 540–549. Google ScholarDigital Library
[72] Le J., X. Liao, and B. Yang. 2017. Full autonomy: A novel individualized anonymity model for privacy preserving 66 (2017), 204–217. Google ScholarDigital Library
[73] Du M. et al. 2017. A differential privacy-based query model for sustainable Fog data centers. IEEE Transactions on Sustainable Computing, 2017.Google Scholar
[74] Huo Y. et al. 2017. LoDPD: A location difference-based proximity detection protocol for Fog computing. IEEE Internet of Things Journal 4, 5 (2017), 1117–1124.Google ScholarCross Ref
[75] Sharma P. K., Chen M.-Y., and Park J. H. J. I. A.. 2018. A software defined Fog node based distributed blockchain Cloud architecture for IoT. IEEE Access 6 (2018), 115–124.Google ScholarCross Ref
[76] Bradbury M., Jhumka A., and Leeke M.. 2018. Hybrid online protocols for source location privacy in wireless sensor networks. Journal of Parallel and Distributed Computing 115 (2018), 67–81.Google ScholarCross Ref
[77] Gope P., R. Amin, S. H. Islam, N. Kumar, and V. K. Bhalla. 2018. Lightweight and privacy-preserving RFID authentication scheme for distributed IoT infrastructure with secure localization services for smart city environment 83 (2018), 629–637. Google ScholarDigital Library
[78] Lu Y. and Sun N.. 2018. A resilient data aggregation method based on spatio-temporal correlation for wireless sensor networks. EURASIP Journal on Wireless Communications and Networking 2018, 1 (2018), 157.Google ScholarCross Ref
[79] De Capitani di Vimercati S. et al. 2018. Enforcing authorizations while protecting access confidentiality. 2018 (Preprint): 1–33.Google Scholar
[80] Cheng K., Hou Y., and Wang L.. 2018. Secure similar sequence query on outsourced genomic data. In Proceedings of the 2018 on Asia Conference on Computer and Communications Security. 2018. ACM. Google ScholarDigital Library
[81] Yan H., X. Li, Y. Wang, and C. Jia. 2018. Centralized duplicate removal video storage system with privacy preservation in IoT. 18, 6 (2018), 1814.Google Scholar
[82] Li X., J. Niu, S. Kumari, F. Wu, A. K. Sangaiah, and K. K. R. Choo. 2018. A three-factor anonymous authentication scheme for wireless sensor networks in Internet of Things environments. 103 (2018), 194–204. Google ScholarDigital Library
[83] Liu X. et al. 2018. Hybrid privacy-preserving clinical decision support system in Fog–Cloud computing. Future Generation Computer Systems 78 (2018), 825–837.Google ScholarCross Ref
[84] Thota C. et al. 2018. Centralized Fog computing security platform for IoT and Cloud in healthcare system. In Exploring the Convergence of Big Data and the Internet of Things. 2018, IGI Global. 141–154.Google ScholarCross Ref
[85] Karopoulos G., Ntantogian C., and Xenakis C.. 2018. MASKER: Masking for privacy-preserving aggregation in the smart grid ecosystem. Computers & Security 73 (2018), 307–325.Google ScholarCross Ref
[86] Tonyali S. et al. 2018. Privacy-preserving protocols for secure and reliable data aggregation in IoT-enabled smart metering systems. 78 (2018), 547–557.Google Scholar
[87] Wang H., Wang Z., and Domingo-Ferrer J. J. F. G. C. S.. 2018. Anonymous and secure aggregation scheme in Fog-based public Cloud computing. Future Generation Computer Systems 78 (2018), 712–719. Google ScholarDigital Library
[88] Koo D. and Hur J.. 2018. Privacy-preserving deduplication of encrypted data with dynamic ownership management in Fog computing. Future Generation Computer Systems 78 (2018), 739–752.Google ScholarCross Ref
[89] Yekta N. I. and Lu R.. 2018. Xrquery: Achieving communication-efficient privacy-preserving query for Fog-enhanced IoT. In 2018 IEEE International Conference on Communications (ICC). 2018. IEEE.Google ScholarCross Ref
[90] Guan Z. et al. 2019. APPA: An anonymous and privacy preserving data aggregation scheme for Fog-enhanced IoT. Journal of Network and Computer Applications 125 (2019), 82–92.Google ScholarCross Ref
[91] Belguith S. et al. 2018. Phoabe: Securely outsourcing multi-authority attribute based encryption with policy hidden for Cloud assisted IoT. Computer Networks 133 (2018), 141–156.Google ScholarCross Ref
[92] Challa S. et al. 2018. An efficient ECC-based provably secure three-factor user authentication and key agreement protocol for wireless healthcare sensor networks. Computers & Electrical Engineering 69 (2018), 534–554.Google ScholarCross Ref
[93] Pasquier T. et al. 2018. Data provenance to audit compliance with privacy policy in the Internet of Things. Personal and Ubiquitous Computing 22, 2 (2018), 333–344. Google ScholarDigital Library
[94] Tao M., J. Zuo, Z. Liu, A. Castiglione, and F. Palmieri. 2018. Multi-layer cloud architectural model and ontology-based security service framework for IoT-based smart homes. 78 (2018), 1040–1051. Google ScholarDigital Library
[95] Kapusta K., Memmi G., and Noura H. J. A. O. T.. 2019. Additively homomorphic encryption and fragmentation scheme for data aggregation inside unattended wireless sensor networks. Annals of Telecommunications 74, 3 (2019), 157--165.Google ScholarCross Ref
[96] Saha R. et al. 2019. Privacy ensurede-healthcare for fog-enhanced IoT based applications. IEEE Access 7 (2019), 44536–44543.Google ScholarCross Ref
[97] Yin X. C. et al. 2019. An IoT-based anonymous function for security and privacy in healthcare sensor networks. Sensors 19, 14 (2019), 3146.Google ScholarCross Ref
[98] Gu J. et al. 2019. A fog computing solution for context-based privacy leakage detection for android healthcare devices. Sensors 19, 5 (2019), 1184.Google ScholarCross Ref
[99] Zhang H. et al. 2019. Video denoising for security and privacy in fog computing. Concurrency and Computation: Practice and Experience 31, 22 (2019), e4763.Google ScholarCross Ref
[100] Liu J.-N. et al. 2019. Enabling efficient and privacy-preserving aggregation communication and function query for fog computing-based smart grid. IEEE Transactions on Smart Grid 11, 1 (2019), 247–257.Google ScholarCross Ref
[101] Wang L., Hu Z., and Liu L.. 2019. Privacy-preserving and dynamic spatial range aggregation query processing in wireless sensor networks. In International Conference on Database Systems for Advanced Applications. 2019. Springer.Google ScholarDigital Library
[102] Wang L. et al. 2019. A novel privacy-and integrity-preserving approach for multidimensional data range queries in two-tiered wireless sensor networks. International Journal of Distributed Sensor Networks 15, 6 (2019), 1550147719855893.Google ScholarCross Ref
[103] Gai K. et al. 2019. Multi-access filtering for privacy-preserving Fog computing. IEEE Transactions on Cloud Computing, 2019.Google Scholar
[104] Alemneh E. et al. 2020. A two-way trust management system for Fog computing. Future Generation Computer Systems 106 (2020), 206–220.Google ScholarDigital Library
[105] Junejo A. K., Komninos N., and McCann J. A.. 2020. A secure integrated framework for Fog-assisted Internet of Things systems. IEEE Internet of Things Journal 2020.Google Scholar
[106] Liu Y., Zhang J., and Zhan J.. 2020. Privacy protection for Fog computing and the Internet of Things data based on blockchain. Cluster Computing (2020), 1–15.Google Scholar
[107] Gu K. et al. 2020. Reusable mesh signature scheme for protecting identity privacy of IoT devices. Sensors 20, 3 (2020), 758.Google ScholarCross Ref
[108] Perera C. et al. 2020. Designing privacy-aware Internet of Things applications. Information Sciences 512 (2020), 238–257.Google ScholarDigital Library
[109] Qu Y. et al. 2020. Decentralized privacy using blockchain-enabled federated learning in fog computing. IEEE Internet of Things Journal 7, 6 (2020), 5171–5183.Google ScholarCross Ref
[110] Baniata H., Anaqreh A., and Kertesz A.. 2021. PF-BTS: A Privacy-aware fog-enhanced blockchain-assisted task scheduling. Information Processing & Management 58, 1 (2021), 102393.Google ScholarCross Ref
[111] Arif M. et al. 2020. SDN based communications privacy-preserving architecture for vanets using fog computing. Vehicular Communications 26 (2020), 100265.Google ScholarCross Ref
[112] Deebak B. and Al-Turjman F.. 2021. Robust lightweight privacy-preserving and session scheme interrogation for fog computing systems. Journal of Information Security and Applications 58 (2021), 102689.Google ScholarCross Ref
[113] Razaq M. M. et al. 2021. Privacy-aware collaborative task offloading in fog computing. IEEE Transactions on Computational Social Systems 2021.Google ScholarCross Ref
[114] Zhou C. et al. 2020. Privacy-preserving federated learning in fog computing. IEEE Internet of Things Journal 7, 11 (2020), 10782–10793.Google ScholarCross Ref
[115] Chen S. et al. 2020. Efficient privacy preserving data collection and computation offloading for fog-assisted IoT. IEEE Transactions on Sustainable Computing 5, 4 (2020), 526–540.Google ScholarCross Ref
[116] Shen X. et al. 2020. A privacy-preserving data aggregation scheme for dynamic groups in fog computing. Information Sciences 514 (2020), 118–130.Google ScholarDigital Library
[117] Zhao S. et al. 2020. Smart and practical privacy-preserving data aggregation for fog-based smart grids. IEEE Transactions on Information Forensics and Security 16 (2020), 521–536.Google ScholarDigital Library
[118] Li W. et al. 2020. Using granule to search privacy preserving voice in home IoT systems. IEEE Access 2020.Google Scholar
[119] Tang W. et al. 2020. Functional privacy-preserving outsourcing scheme with computation verifiability in fog computing. KSII Transactions on Internet & Information Systems 14, 1 (2020).Google Scholar
[120] Mohanty S. N. et al. 2020. An efficient lightweight integrated blockchain (ELIB) model for IoT security and privacy. Future Generation Computer Systems 102 (2020), 1027–1037.Google ScholarDigital Library
[121] Wang Z. et al. 2020. LiPSG: Lightweight privacy-preserving Q-learning based energy management for the IoT-enable smart grid. IEEE Internet of Things Journal 2020.Google Scholar
[122] Li H., Han D., and Tang M.. 2020. A privacy-preserving charging scheme for electric vehicles using blockchain and fog computing. IEEE Systems Journal 2020.Google Scholar
[123] Qi L. et al. 2020. Privacy-aware data fusion and prediction with spatial-temporal context for smart city industrial environment. IEEE Transactions on Industrial Informatics 2020.Google Scholar
[124] Kong Q. et al. 2020. Privacy-preserving continuous data collection for predictive maintenance in vehicular fog-cloud. IEEE Transactions on Intelligent Transportation Systems 2020.Google Scholar
[125] Baniata H., Almobaideen W., and Kertesz A.. 2020. A privacy preserving model for fog-enabled MCC systems using 5G connection. In 2020 Fifth International Conference on Fog and Mobile Edge Computing (FMEC). 2020. IEEE.Google ScholarCross Ref
[126] Kong Q., Su L., and Ma M.. 2020. Achieving privacy-preserving and verifiable data sharing in vehicular fog with blockchain. IEEE Transactions on Intelligent Transportation Systems 2020.Google Scholar
[127] Zeng B. et al. 2020. BRAKE: Bilateral privacy-preserving and accurate task assignment in fog-assisted mobile crowdsensing. IEEE Systems Journal 2020.Google Scholar
[128] Eckhoff D. and Wagner I.. 2017. Privacy in the smart city—Applications, technologies, challenges, and solutions. IEEE Communications Surveys & Tutorials 20, 1 (2017), 489–516.Google ScholarCross Ref
[129] Sun L. et al. 2013. Understanding metropolitan patterns of daily encounters. Proceedings of the National Academy of Sciences 110, 34 (2013), 13774–13779.Google ScholarCross Ref
[130] Iliopoulou C. A. et al. 2020. Identifying spatio-temporal patterns of bus bunching in urban networks. Journal of Intelligent Transportation Systems 24, 4 (2020), 365–382.Google ScholarCross Ref
[131] Liu Y. et al. 2015. Social sensing: A new approach to understanding our socioeconomic environments. Annals of the Association of American Geographers 105, 3 (2015), 512–530.Google ScholarCross Ref
[132] Huang J. et al. 2018. Tracking job and housing dynamics with smartcard data. Proceedings of the National Academy of Sciences 115, 50 (2018), 12710–12715.Google ScholarCross Ref
[133] Luo S., Jin J., and Li J.. 2009. A smart fridge with an ability to enhance health and enable better nutrition. International Journal of Multimedia and Ubiquitous Engineering 4, 2 (2009), 69–80.Google Scholar
[134] Bosma H.. 2020. Increasing nutrient awareness with the Smart Kitchen Scale. 2020, University of Twente.Google Scholar
[135] Ståhlbröst A. et al. 2015. Design of smart city systems from a privacy perspective. IADIS International Journal on WWW/Internet 13, 1 (2015), 1–16.Google Scholar
[136] Memos V. A. et al. 2018. An efficient algorithm for media-based surveillance system (EAMSuS) in IoT smart city framework 83 (2018), 619–628. Google ScholarDigital Library
[137] Gupta B., Agrawal D. P., and Yamaguchi S.. 2016. Handbook of research on modern cryptographic solutions for computer and cyber security. 2016: IGI Global. Google ScholarDigital Library
[138] Dehkordi S. A. et al. 2020. A survey on data aggregation techniques in IoT sensor networks. Wireless Networks 26, 2 (2020), 1243–1263.Google ScholarDigital Library
[139] Randhawa S. and Jain S.. 2020. Energy-Efficient Fuzzy-Logic-Based Data Aggregation in Wireless Sensor Networks. In Information and Communication Technology for Sustainable Development. 2020, Springer, 739–748.Google ScholarCross Ref
[140] Rani S. and Saini P.. 2020. Fog computing: Applications and secure data aggregation, in handbook of computer networks and cyber security. 2020, Springer, 475–492.Google Scholar
[141] Gupta B., Rana S., and Sharma A.. 2020. An efficient data aggregation approach for prolonging lifetime of wireless sensor network. In International Conference on Innovative Computing and Communications. 2020. Springer.Google ScholarCross Ref
[142] Shobana M., Sabitha R., and Karthik S.. 2020. An enhanced soft computing-based formulation for secure data aggregation and efficient data processing in large-scale wireless sensor network. Soft Computing (2020), 1–12.Google Scholar
[143] Hu P. et al. 2020. Efficient location privacy-preserving range query scheme for vehicle sensing systems. Journal of Systems Architecture (2020), 101714.Google ScholarCross Ref
[144] Babu S. S. and Balasubadra K.. 2019. Revamping data access privacy preservation method against inside attacks in wireless sensor networks. Cluster Computing 22, 1 (2019), 65–75.Google ScholarDigital Library
[145] Hathaliya J. J. and Tanwar S.. 2020. An exhaustive survey on security and privacy issues in Healthcare 4.0. Computer Communications 153 (2020), 311–335.Google ScholarDigital Library
[146] Puthal D. et al. 2019. Fog computing security challenges and future directions [energy and security]. IEEE Consumer Electronics Magazine 8, 3 (2019), 92–96.Google ScholarCross Ref
[147] Chakraborty B., Verma S., and Singh K. P.. 2020. Temporal differential privacy in wireless sensor networks. Journal of Network and Computer Applications (2020), 102548.Google ScholarCross Ref
[148] Danezis G.. 2004. The traffic analysis of continuous-time mixes. In International Workshop on Privacy Enhancing Technologies. 2004. Springer. Google ScholarDigital Library
[149] Gruteser M. and Grunwald D.. 2003. Anonymous usage of location-based services through spatial and temporal cloaking. In Proceedings of the 1st International Conference on Mobile Systems, Applications and Services. 2003. Google ScholarDigital Library
[150] Mehta K., Liu D., and Wright M.. 2011. Protecting location privacy in sensor networks against a global eavesdropper. IEEE Transactions on Mobile Computing 11, 2 (2011), 320–336. Google ScholarDigital Library
[151] Kamat P. et al. 2007. Temporal privacy in wireless sensor networks. In 27th International Conference on Distributed Computing Systems (ICDCS'07). 2007. IEEE. Google ScholarDigital Library
[152] Yang Y. et al. 2008. Towards event source unobservability with minimum network traffic in sensor networks. In Proceedings of the first ACM conference on Wireless network security. 2008. Google ScholarDigital Library
[153] Xu M. et al. 2019. Dynamic and disjoint routing mechanism for protecting source location privacy in WSNs. In 2019 15th International Conference on Computational Intelligence and Security (CIS). 2019. IEEE.Google ScholarCross Ref
[154] Mutalemwa L. C. and Shin S.. 2019. Achieving source location privacy protection in monitoring wireless sensor networks through proxy node routing. Sensors 19, 5 (2019), 1037.Google ScholarCross Ref
[155] Ozturk C., Zhang Y., and Trappe W.. 2004. Source-location privacy in energy-constrained sensor network routing. In Proceedings of the 2nd ACM Workshop on Security of Ad Hoc and Sensor Networks. 2004. Google ScholarDigital Library
[156] Kamat P. et al. 2005. Enhancing source-location privacy in sensor network routing. In 25th IEEE International Conference on Distributed Computing Systems (ICDCS'05). 2005. IEEE. Google ScholarDigital Library
[157] Baroutis N. and Younis M.. 2019. Location privacy in wireless sensor networks, in mission-oriented sensor networks and systems: Art and science. 2019, Springer, 669–714.Google Scholar
[158] Hamad S. A. et al. 2020. Realizing an Internet of secure Things: A survey on issues and enabling technologies. IEEE Communications Surveys & Tutorials 22, 2 (2020), 1372–1391.Google ScholarCross Ref
[159] Ali Z. et al. 2020. A robust authentication and access control protocol for securing wireless healthcare sensor networks. Journal of Information Security and Applications 52 (2020), 102502.Google ScholarCross Ref
[160] Henze M. et al. 2016. A comprehensive approach to privacy in the cloud-based Internet of Things 56 (2016), 701–718. Google ScholarDigital Library
[161] Caballero V. et al. 2019. Ontology-defined middleware for Internet of Things architectures. Sensors 19, 5 (2019), 1163.Google ScholarCross Ref
[162] Singh J. et al. 2016. Big ideas paper: Policy-driven middleware for a legally-compliant Internet of Things. In Proceedings of the 17th International Middleware Conference. 2016. Google ScholarDigital Library
[163] Abed A. A.. 2016. Internet of Things (IoT): Architecture and design. In 2016 Al-Sadeq International Conference on Multidisciplinary in IT and Communication Science and Applications (AIC-MITCSA). 2016. IEEE.Google Scholar
[164] J. Srinivas, D. Mishra, S. Mukhopadhyay, and S. Kumari. 2017. Provably secure biometric based authentication and key agreement protocol for wireless sensor networks. Journal of Ambient Intelligence and Humanized Computing 9, 4 (2017), 875--895.Google Scholar
[165] W. Song, B. Wang, Q. Wang, Z. Peng, W. Lou, and Y. Cui. 2017. A privacy-preserved full-text retrieval algorithm over encrypted data for cloud storage applications. Journal of Parallel and Distributed Computing 99 (2017), 14–27.Google ScholarCross Ref
[166] Wang J. et al. 2017. A scalable and privacy-aware IoT service for live video analytics. In Proceedings of the 8th ACM on Multimedia Systems Conference 2017. Google ScholarDigital Library
[167] Hasan R. et al. 2017. Cartooning for enhanced privacy in lifelogging and streaming videos. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops 2017.Google ScholarCross Ref
[168] Zhang T. and Zhu Q.. 2017. Dynamic differential privacy for ADMM-based distributed classification learning. IEEE Transactions on Information Forensics and Security 12, 1 (2017), 172–187. Google ScholarDigital Library
[169] M. Ambrosin, A. Anzanpour, M. Conti, T. Dargahi, S. R. Moosavi, A. M. Rahmani, and P. Liljeberg. 2016. On the feasibility of attribute-based encryption on Internet of Things devices. IEEE Micro 36, 6 (2016), 25–35. Google ScholarDigital Library
[170] Gope P. and Hwang T.. 2016. A realistic lightweight anonymous authentication protocol for securing real-time application data access in wireless sensor networks. IEEE Transactions on Industrial Electronics 63, 11 (2016), 7124–7132.Google ScholarCross Ref
[171] Tewari A. and Gupta B. B.. 2017. Cryptanalysis of a novel ultra-lightweight mutual authentication protocol for IoT devices using RFID tags. The Journal of Supercomputing 73, 3 (2017), 1085–1102. Google ScholarDigital Library
[172] A. K. Sutrala, A. K. Das, V. Odelu, M. Wazid, and S. Kumari. 2016. Secure anonymity-preserving password-based user authentication and session key agreement scheme for telecare medicine information systems. Computer Methods and Programs in Biomedicine 135 (2016), 167–185. Google ScholarDigital Library
[173] Khan Z., Pervez Z., and Abbasi A. G.. 2017. Towards a secure service provisioning framework in a smart city environment. Future Generation Computer Systems 77 (2017), 112–135. Google ScholarDigital Library
[174] Kapusta K., Memmi G., and Noura H.. 2019. Additively homomorphic encryption and fragmentation scheme for data aggregation inside unattended wireless sensor networks. Annals of Telecommunications (2019), 1–9.Google Scholar
[175] Li L. et al. 2018. Flexible and secure data transmission system based on semi-tensor compressive sensing in wireless body area networks. IEEE Internet of Things Journal 2018.Google Scholar
[176] Gilbert E. P. K. et al. 2018. Trust based data prediction, aggregation and reconstruction using compressed sensing for clustered wireless sensor networks. Computers & Electrical Engineering 2018.Google ScholarCross Ref
[177] Troncoso-Pastoriza J. R., Gonzalez-Jimenez D., and Perez-Gonzalez F., Fully private noninteractive face verification. IEEE Transactions on Information Forensics and Security 8, 7 (2013), 1101–1114. Google ScholarDigital Library
[178] Wu Q. et al. 2016. Privacy-aware multipath video caching for content-centric networks. IEEE Journal on Selected Areas in Communications 34, 8 (2016), 2219–2230. Google ScholarDigital Library
[179] Castelluccia C. et al. 2009. Efficient and provably secure aggregation of encrypted data in wireless sensor networks. ACM Transactions on Sensor Networks (TOSN) 5, 3 (2009), 20. Google ScholarDigital Library
[180] Marvin S. et al. 2018. Urban Living Labs: Experimenting with City Futures. 2018: Routledge.Google Scholar
[181] Korayem M. et al. 2016. Enhancing lifelogging privacy by detecting screens. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. 2016. Google ScholarDigital Library
[182] Gope P. et al. 2018. Lightweight and privacy-preserving RFID authentication scheme for distributed IoT infrastructure with secure localization services for smart city environment. Future Generation Computer Systems 83 (2018), 629–637. Google ScholarDigital Library
[183] Li R. et al. 2017. IoT applications on secure smart shopping system. IEEE Internet of Things Journal 4, 6 (2017), 1945–1954.Google ScholarCross Ref
[184] Shao M. et al. 2009. Cross-layer enhanced source location privacy in sensor networks. In 2009 6th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks. 2009. IEEE. Google ScholarDigital Library
[185] Lin X. and Li X.. 2013. Achieving efficient cooperative message authentication in vehicular ad hoc networks. IEEE Transactions on Vehicular Technology 62, 7 (2013), 3339–3348.Google ScholarCross Ref
[186] Huang Q., Yang Y., and Wang L. J. I. A.. 2017. Secure data access control with ciphertext update and computation outsourcing in fog computing for Internet of Things. IEEE Access 5 (2017), 12941–12950.Google ScholarCross Ref
[187] Chen J.-h. et al. 2008. Lightning location system and lightning detection network of China power grid. High Voltage Engineering 34, 3 (2008), 425–431.Google Scholar
[188] Basudan S., Lin X., and Sankaranarayanan K.. 2017. A privacy-preserving vehicular crowdsensing-based road surface condition monitoring system using fog computing. IEEE Internet of Things Journal 4, 3 (2017), 772–782.Google ScholarCross Ref
[189] Gheisari M., Wang G., and Chen S.. 2020. An edge computing-enhanced Internet of Things framework for privacy-preserving in smart city. Computers & Electrical Engineering 81 (2020), 106504.Google ScholarDigital Library
[190] Ma Z. et al. 2020. Lightweight privacy-preserving medical diagnosis in edge computing. IEEE Transactions on Services Computing 2020.Google ScholarCross Ref
[191] Zhao P. et al. 2020. P 3: Privacy-preserving scheme against poisoning attacks in mobile-edge computing. IEEE Transactions on Computational Social Systems 7, 3 (2020), 818–826.Google ScholarCross Ref
[192] Zhao Y. et al. 2020. Privacy-preserving blockchain-based federated learning for IoT devices. IEEE Internet of Things Journal 2020.Google ScholarCross Ref
[193] Wang H., Wang Z., and Domingo-Ferrer J.. 2018. Anonymous and secure aggregation scheme in fog-based public cloud computing. Future Generation Computer Systems 78 (2018), 712–719. Google ScholarDigital Library
[194] Sarwar K. et al. 2021. Lightweight, divide-and-conquer privacy-preserving data aggregation in fog computing. Future Generation Computer Systems 2021.Google ScholarCross Ref
[195] Bao H. and Lu R.. 2017. A lightweight data aggregation scheme achieving privacy preservation and data integrity with differential privacy and fault tolerance. Peer-to-Peer Networking and Applications 10, 1 (2017), 106–121.Google ScholarCross Ref
[196] Bao H. and Lu R.. 2016. Comment on “privacy-enhanced data aggregation scheme against internal attackers in smart grid”. IEEE Transactions on Industrial Informatics 12, 1 (2016), 2–5. Google ScholarDigital Library
[197] Chen L., Lu R., and Cao Z.. 2015. PDAFT: A privacy-preserving data aggregation scheme with fault tolerance for smart grid communications. Peer-to-Peer Networking and Applications 8, 6 (2015), 1122–1132.Google ScholarCross Ref
[198] Xiao M. et al. 2017. A hybrid scheme for fine-grained search and access authorization in fog computing environment. Sensors 17, 6 (2017), 1423.Google ScholarCross Ref
[199] Mitton N. et al. 2012. Combining Cloud and Sensors in a Smart City Environment. 2012, Springer.Google Scholar
[200] Cui J. et al. 2020. Edge computing in VANETs-an efficient and privacy-preserving cooperative downloading scheme. IEEE Journal on Selected Areas in Communications 38, 6 (2020), 1191–1204.Google ScholarCross Ref
[201] Bi M. et al. 2020. A privacy-preserving mechanism based on local differential privacy in edge computing. China Communications 17, 9 (2020), 50–65.Google ScholarCross Ref
[202] Mitrokotsa A., Onete C., and Vaudenay S.. 2014. Location leakage in distance bounding: Why location privacy does not work. Computers & Security 45 (2014), 199–209. Google ScholarDigital Library
[203] Yeh L.-Y. et al. 2018. Cloud-based fine-grained health information access control framework for lightweight IoT devices with dynamic auditing and attribute revocation. IEEE Transactions on Cloud Computing 6, 2 (2018), 532–544.Google ScholarCross Ref
[204] Ab Rahman N. H. et al. 2016. Forensic-by-design framework for cyber-physical cloud systems. IEEE Cloud Computing 3, 1 (2016), 50–59.Google ScholarCross Ref
[205] Wu F. et al. 2017. A lightweight and anonymous RFID tag authentication protocol with cloud assistance for e-healthcare applications. Journal of Ambient Intelligence and Humanized Computing 2017.Google Scholar
[206] Shen H., Zhang M., and Shen J.. 2017. Efficient privacy-preserving cube-data aggregation scheme for smart grids. IEEE Trans. Information Forensics and Security 12, 6 (2017), 1369–1381. Google ScholarDigital Library
[207] Figueres N. B. et al. 2016. Efficient smart metering based on homomorphic encryption. Computer Communications 82 (2016), 95–101.Google ScholarCross Ref
[208] Ivaşcu T., Frîncu M., and Negru V.. 2016. Considerations towards security and privacy in Internet of Things based eHealth applications. In Intelligent Systems and Informatics (SISY), 2016 IEEE 14th International Symposium on. 2016. IEEE.Google Scholar
[209] Mahmood K. et al. 2018. An elliptic curve cryptography based lightweight authentication scheme for smart grid communication. Future Generation Comp. Syst. 81 (2018), 557–565. Google ScholarDigital Library
[210] Challa S. et al. 2017. An efficient ECC-based provably secure three-factor user authentication and key agreement protocol for wireless healthcare sensor networks. Computers & Electrical Engineering, 2017.Google Scholar
[211] Song T. et al. 2017. A privacy preserving communication protocol for IoT applications in smart homes. IEEE Internet of Things Journal 4, 6 (2017), 1844–1852.Google ScholarCross Ref
[212] Chifor B.-C. et al. 2017. A security authorization scheme for smart home Internet of Things devices. Future Generation Computer Systems 2017.Google Scholar
[213] Amin R. et al. 2018. A robust and anonymous patient monitoring system using wireless medical sensor networks. Future Generation Comp. Syst. 80 (2018), 483–495. Google ScholarDigital Library
[214] Askoxylakis I. et al. 2016. Computer Security–ESORICS 2016. 2016: Springer.Google Scholar
[215] Peng M. et al. 2016. Fog-computing-based radio access networks: Issues and challenges. IEEE Network 30, 4 (2016), 46–53. Google ScholarDigital Library
[216] Peng M. et al. 2014. Heterogeneous cloud radio access networks: A new perspective for enhancing spectral and energy efficiencies. IEEE Wireless Communications 21, 6 (2014), 126–135.Google ScholarCross Ref
[217] Peng M. et al. 2015. System architecture and key technologies for 5G heterogeneous cloud radio access networks. IEEE Network 29, 2 (2015), 6–14.Google ScholarDigital Library
[218] Liu J. et al. 2018. Secure intelligent traffic light control using fog computing. Future Generation Computer Systems 78 (2018), 817–824. Google ScholarDigital Library
[219] Yang R. et al. 2018. Position based cryptography with location privacy: A step for fog computing. Future Generation Computer Systems 78 (2018), 799–806. Google ScholarDigital Library
[220] Ali B., Gregory M. A., and Li S.. 2021. Multi-access edge computing architecture, data security and privacy: A review. IEEE Access 2021.Google Scholar
[221] Suri B. et al. 2019. Peering through the fog: An inter-fog communication approach for computing environment. In International Conference on Innovative Computing and Communications. 2019. Springer.Google ScholarCross Ref
[222] Mostafavi S. and Shafik W.. 2019. Fog computing architectures, privacy and security solutions. Journal of Communications Technology, Electronics and Computer Science 24 (2019), 1–14.Google Scholar

Index Terms

A Survey on Privacy Preservation in Fog-Enabled Internet of Things
1. General and reference
  1. Document types
    1. Surveys and overviews

Recommendations

Privacy preserving Internet of Things

The Internet of Things (IoT) is the latest web evolution that incorporates billions of devices that are owned by different organisations and people who are deploying and using them for their own purposes. IoT-enabled harnessing of the information that ...
Read More
Privacy-Preserving Key Agreement Protocol for Fog Computing Supported Internet of Things Environment
Abstract
Fog computing is an emerging paradigm that provides confluence facilities between Internet of Things (IoT) devices and cloud. The fog nodes process the information collected by the IoT devices, thereby providing services to the cloud. With the ...
Read More
A survey on privacy and security of Internet of Things
Abstract
Internet of Things (IoT) has fundamentally changed the way information technology and communication environments work, with significant advantages derived from wireless sensors and nanotechnology, among others. While IoT is still a ...
Read More

Comments

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

Published in
ACM Computing Surveys Volume 55, Issue 1
January 2023
860 pages
ISSN:0360-0300
EISSN:1557-7341
DOI:10.1145/3492451
Editor:
Albert Zomaya
University of Sydney, Australia
Issue’s Table of Contents
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
In-Cooperation
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
- Published: 23 November 2021
- Accepted: 1 July 2021
- Revised: 1 March 2021
- Received: 1 May 2020
Published in csur Volume 55, Issue 1

Permissions
Request permissions about this article.
Request Permissions

Check for updates
Author Tags
Content privacy
context privacy
fog computing
Fog-enabled IoT
Internet of Things
IoT privacy
Qualifiers
- survey
- Refereed
Conference
Funding Sources
Other Metrics
View Article Metrics

Article Metrics
- 0
  Total Citations
  View Citations
- 1,478
  Total Downloads
- Downloads (Last 12 months)366
- Downloads (Last 6 weeks)47
Other Metrics
View Author Metrics
Cited By
This publication has not been cited yet

PDF Format

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Full Text

View this article in Full Text.

View Full Text

HTML Format

View this article in HTML Format .

View HTML Format

A Survey on Privacy Preservation in Fog-Enabled Internet of Things

ACM Computing Surveys

Abstract

REFERENCES

Cited By

Index Terms

Recommendations

Privacy preserving Internet of Things

Privacy-Preserving Key Agreement Protocol for Fog Computing Supported Internet of Things Environment

A survey on privacy and security of Internet of Things