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Security Approaches for Data Provenance in the Internet of Things: A Systematic Literature Review (2407.03466v2)

Published 3 Jul 2024 in cs.CR

Abstract: The Internet of Things (IoT) relies on resource-constrained devices deployed in unprotected environments. Given their constrained nature, IoT systems are vulnerable to security attacks. Data provenance, which tracks the origin and flow of data, provides a potential solution to guarantee data security, including trustworthiness, confidentiality, integrity, and availability in IoT systems. Different types of risks may be faced during data transmission in single-hop and multi-hop scenarios, particularly due to the interconnectivity of IoT systems, which introduces security and privacy concerns. Attackers can inject malicious data or manipulate data without notice, compromising data integrity and trustworthiness. Data provenance offers a way to record the origin, history, and handling of data to address these vulnerabilities. A systematic literature review of data provenance in IoT is presented, exploring existing techniques, practical implementations, security requirements, and performance metrics. Respective contributions and shortcomings are compared. A taxonomy related to the development of data provenance in IoT is proposed. Open issues are identified, and future research directions are presented, providing useful insights for the evolution of data provenance research in the context of the IoT.

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References (160)
  1. Raja Naeem Akram and Ryan K. L. Ko. 2014. Unified Model for Data Security - A Position Paper. In Proceedings of the 2014 IEEE 13th International Conference on Trust, Security and Privacy in Computing and Communications (TRUSTCOM ’14). IEEE Computer Society, USA, 831–839. https://doi.org/10.1109/TrustCom.2014.110
  2. Wireless sensor networks: a survey. Computer Networks 38, 4 (2002), 393–422. https://doi.org/10.1016/S1389-1286(01)00302-4
  3. Mabrook S. Al-Rakhami and Majed Al-Mashari. 2022. ProChain: Provenance-Aware Traceability Framework for IoT-Based Supply Chain Systems. IEEE Access 10 (2022), 3631–3642. https://doi.org/10.1109/ACCESS.2021.3135371
  4. Md Morshed Alam and Weichao Wang. 2021. A Comprehensive Survey on Data Provenance: State-of-the-Art Approaches and Their Deployments for IoT Security Enforcement. J. Comput. Secur. 29, 4 (jan 2021), 423–446. https://doi.org/10.3233/JCS-200108
  5. S.M. Iftekharul Alam and Sonia Fahmy. 2014. A practical approach for provenance transmission in wireless sensor networks. Ad Hoc Networks 16 (2014), 28–45. https://doi.org/10.1016/j.adhoc.2013.12.001
  6. RPL: IPv6 Routing Protocol for Low-Power and Lossy Networks. RFC 6550. https://doi.org/10.17487/RFC6550
  7. Securing First-Hop Data Provenance for Bodyworn Devices Using Wireless Link Fingerprints. IEEE Transactions on Information Forensics and Security 9, 12 (2014), 2193–2204. https://doi.org/10.1109/TIFS.2014.2357998
  8. Bio-inspired Think-and-Share Optimization for Big Data Provenance in Wireless Sensor Networks. International Journal of Advanced Computer Science and Applications 10, 6 (2019). https://doi.org/10.14569/IJACSA.2019.0100650
  9. Adel Alkhalil and Rabie A. Ramadan. 2017. IoT Data Provenance Implementation Challenges. Procedia Computer Science 109 (2017), 1134–1139. https://doi.org/10.1016/j.procs.2017.05.436 8th International Conference on Ambient Systems, Networks and Technologies, ANT-2017 and the 7th International Conference on Sustainable Energy Information Technology, SEIT 2017, 16-19 May 2017, Madeira, Portugal.
  10. Data Provenance for IoT With Light Weight Authentication and Privacy Preservation. IEEE Internet of Things Journal 6, 6 (2019), 10441–10457. https://doi.org/10.1109/JIOT.2019.2939286
  11. A Lightweight Protocol for Secure Data Provenance in the Internet of Things Using Wireless Fingerprints. IEEE Systems Journal 15, 2 (2021), 2948–2958. https://doi.org/10.1109/JSYST.2020.3000269
  12. Data provenance collection and security in a distributed environment: a survey. International Journal of Computers and Applications 43, 1 (2021), 11–25. https://doi.org/10.1080/1206212X.2018.1501937
  13. Internet of Things: A survey on the security of IoT frameworks. Journal of Information Security and Applications 38 (2018), 8–27. https://doi.org/10.1016/j.jisa.2017.11.002
  14. Evaluation and Analysis of Reversible Watermarking Techniques in WSN for Secure, Lightweight Design of IoT Applications: A Survey. In Advances in Information and Communication, Kohei Arai (Ed.). Springer Nature Switzerland, Cham, 695–708.
  15. Securing Data Provenance in the Cloud. In Open Problems in Network Security, Jan Camenisch and Dogan Kesdogan (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 145–160. https://doi.org/10.1007/978-3-642-27585-2_12
  16. Redactable Blockchain - Or - Rewriting History in Bitcoin and Friends. In Proceedings - 2nd IEEE European Symposium on Security and Privacy, EuroS and P 2017. Institute of Electrical and Electronics Engineers Inc., 111 – 126. https://doi.org/10.1109/EuroSP.2017.37
  17. The Internet of Things: A survey. Computer Networks 54, 15 (2010), 2787–2805. https://doi.org/10.1016/j.comnet.2010.05.010
  18. Evolving Advanced Persistent Threat Detection using Provenance Graph and Metric Learning. In 2020 IEEE Conference on Communications and Network Security (CNS). 1–9. https://doi.org/10.1109/CNS48642.2020.9162264
  19. Data Lineage in Malicious Environments. IEEE Transactions on Dependable and Secure Computing 13, 2 (2016), 178–191. https://doi.org/10.1109/TDSC.2015.2399296
  20. Trusted Tamper-Evident Data Provenance. In 2015 IEEE Trustcom/BigDataSE/ISPA, Vol. 1. 646–653. https://doi.org/10.1109/Trustcom.2015.430
  21. Securing Data Provenance in Internet of Things (IoT) Systems. In Service-Oriented Computing – ICSOC 2016 Workshops, Khalil Drira, Hongbing Wang, Qi Yu, Yan Wang, Yuhong Yan, François Charoy, Jan Mendling, Mohamed Mohamed, Zhongjie Wang, and Sami Bhiri (Eds.). Springer International Publishing, 92–98. https://doi.org/10.1007/978-3-319-68136-8_9
  22. Mining Data Provenance to Detect Advanced Persistent Threats. In 11th International Workshop on Theory and Practice of Provenance (TaPP 2019). USENIX Association, Philadelphia, PA, 1–11. https://www.usenix.org/conference/tapp2019/presentation/barre
  23. Ghita Berrada and James Cheney. 2019. Aggregating unsupervised provenance anomaly detectors. In 11th International Workshop on Theory and Practice of Provenance (TaPP 2019). USENIX Association, Philadelphia, PA, 1–9. https://www.usenix.org/conference/tapp2019/presentation/berrada
  24. A baseline for unsupervised advanced persistent threat detection in system-level provenance. Future Generation Computer Systems 108 (2020), 401–413. https://doi.org/10.1016/j.future.2020.02.015
  25. Elisa Bertino. 2018. Security and Privacy in the IoT. In International Conference on Information Security and Cryptology. Springer, Cham, 3–10. https://doi.org/10.1007/978-3-319-75160-3_1
  26. Integration of Cloud computing and Internet of Things: A survey. Future Generation Computer Systems 56 (2016), 684–700. https://doi.org/10.1016/j.future.2015.09.021
  27. Security of Sanitizable Signatures Revisited. In Public Key Cryptography – PKC 2009. Springer Berlin Heidelberg, Berlin, Heidelberg, 317–336. https://doi.org/10.1007/978-3-642-00468-1_18
  28. Keyless Signatures’ Infrastructure: How to Build Global Distributed Hash-Trees. In Secure IT Systems, Hanne Riis Nielson and Dieter Gollmann (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 313–320.
  29. Efficient Record-Level Keyless Signatures for Audit Logs. In Secure IT Systems, Karin Bernsmed and Simone Fischer-Hübner (Eds.). Springer International Publishing, Cham, 149–164.
  30. Why and Where: A Characterization of Data Provenance. In Database Theory — ICDT 2001, Jan Van den Bussche and Victor Vianu (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 316–330. https://doi.org/10.1007/3-540-44503-X_20
  31. Why and Where: A Characterization of Data Provenance. In Database Theory — ICDT 2001, Jan Van den Bussche and Victor Vianu (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 316–330.
  32. Sahin Buyrukbilen and Spiridon Bakiras. 2014. Secure Similar Document Detection with Simhash. In Secure Data Management, Willem Jonker and Milan Petković (Eds.). Springer International Publishing, Cham, 61–75. https://doi.org/10.1007/978-3-319-06811-4_12
  33. Christian Cachin. 2016. Architecture of the hyperledger blockchain fabric. In Workshop on distributed cryptocurrencies and consensus ledgers, Vol. 310. 4–8. https://www.zurich.ibm.com/dccl/papers/cachin_dccl.pdf
  34. Chameleon-Hashes with Ephemeral Trapdoors. In Public-Key Cryptography – PKC 2017, Serge Fehr (Ed.). Springer Berlin Heidelberg, Berlin, Heidelberg, 152–182. https://doi.org/10.1007/978-3-662-54388-7_6
  35. SmartProvenance: User-friendly provenance system for internet of things applications based on event flow graphs. IET Software 16 (2022), 576–602. https://doi.org/10.1049/sfw2.12071
  36. Moses S. Charikar. 2002. Similarity Estimation Techniques from Rounding Algorithms. In Proceedings of the Thiry-Fourth Annual ACM Symposium on Theory of Computing (Montreal, Quebec, Canada) (STOC ’02). Association for Computing Machinery, New York, NY, USA, 380–388. https://doi.org/10.1145/509907.509965
  37. Towards an Evidence-Based Understanding of Electronic Data Sources. In Proceedings of the 14th International Conference on Evaluation and Assessment in Software Engineering (UK) (EASE’10). BCS Learning & Development Ltd., Swindon, GBR, 135–138. https://doi.org/doi/10.5555/2227057.2227074
  38. Provenance in Databases: Why, How, and Where. Foundations and Trends® in Databases 1, 4 (2009), 379–474. https://doi.org/10.1561/1900000006
  39. ProvNet: Networked bi-directional blockchain for data sharing with verifiable provenance. J. Parallel and Distrib. Comput. 166 (2022), 32–44. https://doi.org/10.1016/j.jpdc.2022.04.003
  40. Secure Data Provenance in Home Energy Monitoring Networks. In Proceedings of the 3rd Annual Industrial Control System Security Workshop (San Juan, PR, USA) (ICSS 2017). Association for Computing Machinery, New York, NY, USA, 7–14. https://doi.org/10.1145/3174776.3174778
  41. Samsung Electronics Co. Accessed 1 August 2023. SmartThings Groovy IDE. https://graph.api.smartthings.com/
  42. Digital Watermarking and Steganography. Morgan Kaufmann Publishers Inc. https://doi.org/10.1016/B978-0-12-372585-1.X5001-3
  43. Tracing the Lineage of View Data in a Warehousing Environment. ACM Trans. Database Syst. 25, 2 (jun 2000), 179–227. https://doi.org/10.1145/357775.357777
  44. An Approach to Evaluate Data Trustworthiness Based on Data Provenance. In Secure Data Management, Willem Jonker and Milan Petković (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 82–98. https://doi.org/10.1007/978-3-540-85259-9_6
  45. DeepLog: Anomaly Detection and Diagnosis from System Logs through Deep Learning. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security (Dallas, Texas, USA) (CCS ’17). Association for Computing Machinery, New York, NY, USA, 1285–1298. https://doi.org/10.1145/3133956.3134015
  46. Contiki - a lightweight and flexible operating system for tiny networked sensors. In 29th Annual IEEE International Conference on Local Computer Networks. 455–462. https://doi.org/10.1109/LCN.2004.38
  47. Mahmoud Elkhodr and Belal Alsinglawi. 2020. Data provenance and trust establishment in the Internet of Things. Security and Privacy 3 (11 2020), 1–11. https://doi.org/10.1002/spy2.99
  48. A Middleware for the Internet of Things. International Journal of Computer Networks and Communications 8 (04 2016), 159–178. https://doi.org/10.5121/ijcnc.2016.8214
  49. Mehdi Fallahpour and David Megías. 2015. Audio Watermarking Based on Fibonacci Numbers. IEEE/ACM Transactions on Audio, Speech, and Language Processing 23, 8 (2015), 1273–1282. https://doi.org/10.1109/TASLP.2015.2430818
  50. I. Foster. 2003. The virtual data grid: a new model and architecture for data-intensive collaboration. In 15th International Conference on Scientific and Statistical Database Management, 2003. 11–. https://doi.org/10.1109/SSDM.2003.1214945
  51. A Survey on Management of Data Provenance: A Survey on Management of Data Provenance. Chinese Journal of Computers 33 (2010), 373–389.
  52. SAQL: A Stream-based Query System for Real-Time Abnormal System Behavior Detection. In 27th USENIX Security Symposium (USENIX Security 18). USENIX Association, Baltimore, MD, 639–656. https://www.usenix.org/conference/usenixsecurity18/presentation/gao-peng
  53. AIQL: Enabling Efficient Attack Investigation from System Monitoring Data. In 2018 USENIX Annual Technical Conference (USENIX ATC 18). USENIX Association, Boston, MA, 113–126. https://www.usenix.org/conference/atc18/presentation/gao
  54. Pathfinder: Robust path reconstruction in large scale sensor networks with lossy links. In 2013 21st IEEE International Conference on Network Protocols (ICNP). 1–10. https://doi.org/10.1109/ICNP.2013.6733600
  55. Mathieu Garchery and Michael Granitzer. 2020. ADSAGE: Anomaly Detection in Sequences of Attributed Graph Edges applied to insider threat detection at fine-grained level. CoRR abs/2007.06985 (2020). arXiv:2007.06985 https://arxiv.org/abs/2007.06985
  56. Boris Glavic and Klaus R. Dittrich. 2007. Data Provenance: A Categorization of Existing Approaches. In Datenbanksysteme für Business, Technologie und Web. 227–241. https://www.zora.uzh.ch/id/eprint/24450/
  57. Collection tree protocol. In Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems, SenSys 2009. 1–14. https://doi.org/10.1145/1644038.1644040
  58. Chhaya S Gosavi and Suresh N Mali. 2015. Video authentication and copyright protection using unique watermark generation technique and singular value decomposition. International Journal of Computer Applications 123, 3 (2015). https://doi.org/10.5120/ijca2015905255
  59. Paul Groth and Luc Moreau. 2013. PROV-Overview: An Overview of the PROV Family of Documents. https://www.w3.org/TR/2013/NOTE-prov-overview-20130430/
  60. Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems 29, 7 (2013), 1645–1660. https://doi.org/10.1016/j.future.2013.01.010
  61. Emrullah Gultekin and Mehmet S. Aktas. 2022. Systematic Literature Review on Data Provenance in Internet of Things. In Computational Science and Its Applications – ICCSA 2022 Workshops, Osvaldo Gervasi, Beniamino Murgante, Sanjay Misra, Ana Maria A. C. Rocha, and Chiara Garau (Eds.). Springer International Publishing, Cham, 31–46. https://doi.org/10.1007/978-3-031-10542-5_3
  62. Hala Hamadeh and Akhilesh Tyagi. 2021. An FPGA Implementation of Privacy Preserving Data Provenance Model Based on PUF for Secure Internet of Things. SN Computer Science 2, 2 (30 Jan 2021), 65. https://doi.org/10.1007/s42979-020-00428-0
  63. FRAPpuccino: Fault-detection through Runtime Analysis of Provenance. In 9th USENIX Workshop on Hot Topics in Cloud Computing (HotCloud 17). USENIX Association, Santa Clara, CA, 1–7. https://www.usenix.org/conference/hotcloud17/program/presentation/han
  64. Bloom Filter Based Low-Latency Provenance Embedding Schemes in Wireless Networks. In 2020 IEEE Wireless Communications and Networking Conference (WCNC). 1–7. https://doi.org/10.1109/WCNC45663.2020.9120640
  65. Preventing History Forgery with Secure Provenance. ACM Trans. Storage 5, 4, Article 12 (dec 2009), 43 pages. https://doi.org/10.1145/1629080.1629082
  66. Preventing history forgery with secure provenance. ACM Transactions on Storage (TOS) 5, 4 (2009), 1–43. https://doi.org/10.1145/1629080.1629082
  67. Tactical Provenance Analysis for Endpoint Detection and Response Systems. In 2020 IEEE Symposium on Security and Privacy (SP). 1172–1189. https://doi.org/10.1109/SP40000.2020.00096
  68. A survey on data provenance in IoT. World Wide Web 23, 2 (01 Mar 2020), 1441–1463. https://doi.org/10.1007/s11280-019-00746-1
  69. Secure data provenance compression using arithmetic coding in wireless sensor networks. In 2014 IEEE 33rd International Performance Computing and Communications Conference (IPCCC). 1–10. https://doi.org/10.1109/PCCC.2014.7017068
  70. Data provenance for cloud forensic investigations, security, challenges, solutions and future perspectives: A survey. Journal of King Saud University - Computer and Information Sciences 34, 10, Part B (2022), 10217–10245. https://doi.org/10.1016/j.jksuci.2022.10.018
  71. H. V. Jagadish and Frank Olken. 2004. Database Management for Life Sciences Research. SIGMOD Rec. 33, 2 (jun 2004), 15–20. https://doi.org/10.1145/1024694.1024697
  72. Security-aware provenance for transparency in IoT data propagation. IEEE Access 11 (29 May 2023), 55677–55691. https://doi.org/10.1109/ACCESS.2023.3280928 Publisher Copyright: Author.
  73. Prov-IoT: A Security-Aware IoT Provenance Model. In 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). 1360–1367. https://doi.org/10.1109/TrustCom50675.2020.00183
  74. Reema Jain and Manish Jain. 2015. Digital image watermarking using 3-level DWT and FFT via image compression. International Journal of Computer Applications 124, 16 (2015). https://doi.org/10.5120/ijca2015905808
  75. Secure provenance using an authenticated data structure approach. Comput. Secur. 73 (2018), 34–56. https://api.semanticscholar.org/CorpusID:3463031
  76. BlockPro: Blockchain Based Data Provenance and Integrity for Secure IoT Environments. In Proceedings of the 1st Workshop on Blockchain-Enabled Networked Sensor Systems (Shenzhen, China) (BlockSys’18). Association for Computing Machinery, New York, NY, USA, 13–18. https://doi.org/10.1145/3282278.3282281
  77. Michigan Molecular Interactions (MiMI): putting the jigsaw puzzle together. Nucleic Acids Research 35, suppl1 (11 2006), D566–D571. https://doi.org/10.1093/nar/gkl859
  78. Provenance Capture and Use in a Satellite Data Processing Pipeline. IEEE Transactions on Geoscience and Remote Sensing 51, 11 (2013), 5090–5097. https://doi.org/10.1109/TGRS.2013.2266929
  79. Mohsin Kamal and Muhammad Tariq. 2018. Light-Weight Security and Data Provenance for Multi-Hop Internet of Things. IEEE Access 6 (2018), 34439–34448. https://doi.org/10.1109/ACCESS.2018.2850821
  80. Hardware and Embedded Security in the Context of Internet of Things. In Proceedings of the 2013 ACM Workshop on Security, Privacy & Dependability for Cyber Vehicles (Berlin, Germany) (CyCAR ’13). Association for Computing Machinery, New York, NY, USA, 61–64. https://doi.org/10.1145/2517968.2517976
  81. How Was Your Journey? Uncovering Routing Dynamics in Deployed Sensor Networks with Multi-Hop Network Tomography. In Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems (Toronto, Ontario, Canada) (SenSys ’12). Association for Computing Machinery, New York, NY, USA, 15–28. https://doi.org/10.1145/2426656.2426659
  82. A fragile zero watermarking scheme to detect and characterize malicious modifications in database relations. The Scientific World Journal 2013 (2013). https://doi.org/10.1155/2013/796726
  83. Systematic literature reviews in software engineering – A systematic literature review. Information and Software Technology 51, 1 (2009), 7–15. https://doi.org/10.1016/j.infsof.2008.09.009 Special Section - Most Cited Articles in 2002 and Regular Research Papers.
  84. Using mapping studies as the basis for further research – A participant-observer case study. Information and Software Technology 53, 6 (2011), 638–651. https://doi.org/10.1016/j.infsof.2010.12.011 Special Section: Best papers from the APSEC.
  85. xJsnark: A Framework for Efficient Verifiable Computation. In 2018 IEEE Symposium on Security and Privacy (SP). 944–961. https://doi.org/10.1109/SP.2018.00018
  86. Hugo Krawczyk and Tal Rabin. 1998. Chameleon hashing and signatures. Cryptology ePrint Archive (1998).
  87. Mihai T. Lazarescu. 2017. Wireless Sensor Networks for the Internet of Things: Barriers and Synergies. Springer International Publishing, Chapter 9, 155–186. https://doi.org/10.1007/978-3-319-42304-3_9
  88. Digital provenance: Enabling secure data forensics in cloud computing. Future Generation Computer Systems 37 (2014), 259–266. https://doi.org/10.1016/j.future.2013.10.006
  89. Provenance-Based Trustworthiness Assessment in Sensor Networks. In Proceedings of the Seventh International Workshop on Data Management for Sensor Networks (Singapore) (DMSN ’10). Association for Computing Machinery, New York, NY, USA, 2–7. https://doi.org/10.1145/1858158.1858162
  90. Secure and Efficient Distributed Network Provenance for IoT: A Blockchain-Based Approach. IEEE Internet of Things Journal 7, 8 (2020), 7564–7574. https://doi.org/10.1109/JIOT.2020.2988481
  91. Zhe Liu and Yuting Wu. 2020. An Index-Based Provenance Compression Scheme for Identifying Malicious Nodes in Multihop IoT Network. IEEE Internet of Things Journal 7, 5 (2020), 4061–4071. https://doi.org/10.1109/JIOT.2019.2961431
  92. Data Verification and Privacy in IoT Architecture. In 2019 IEEE World Congress on Services (SERVICES), Vol. 2642-939X. 66–71. https://doi.org/10.1109/SERVICES.2019.00026
  93. Traceability and visual analytics for the Internet-of-Things (IoT) architecture. World Wide Web 21, 1 (01 Jan 2018), 7–32. https://doi.org/10.1007/s11280-017-0461-1
  94. PathZip: Packet path tracing in wireless sensor networks. In 2012 IEEE 9th International Conference on Mobile Ad-Hoc and Sensor Systems (MASS 2012). 380–388. https://doi.org/10.1109/MASS.2012.6502538
  95. A Visual Text Mining approach for Systematic Reviews. In First International Symposium on Empirical Software Engineering and Measurement (ESEM 2007). 245–254. https://doi.org/10.1109/ESEM.2007.21
  96. Narayanan Manikandan and Srinivasan Subha. 2018. Parallel AES algorithm for performance improvement in data analytics security for IoT. International Journal of Networking and Virtual Organisations 18, 2 (2018), 112–129. https://doi.org/10.1504/IJNVO.2018.091575
  97. David Megías. 2021. Data Hiding: New Opportunities for Security and Privacy?. In Proceedings of the European Interdisciplinary Cybersecurity Conference (Rennes, France) (EICC 2020). Article 15, 6 pages. https://doi.org/10.1145/3424954.3425511
  98. David Megías. 2015. Improved Privacy-Preserving P2P Multimedia Distribution Based on Recombined Fingerprints. IEEE Transactions on Dependable and Secure Computing 12, 2 (2015), 179–189. https://doi.org/10.1109/TDSC.2014.2320712
  99. Efficient self-synchronised blind audio watermarking system based on time domain and FFT amplitude modification. Signal Processing 90, 12 (2010), 3078–3092. https://doi.org/10.1016/j.sigpro.2010.05.012
  100. The W3C PROV Family of Specifications for Modelling Provenance Metadata. In Proceedings of the 16th International Conference on Extending Database Technology (Genoa, Italy) (EDBT ’13). Association for Computing Machinery, New York, NY, USA, 773–776. https://doi.org/10.1145/2452376.2452478
  101. A Templating System to Generate Provenance. IEEE Transactions on Software Engineering 44, 2 (2018), 103–121. https://doi.org/10.1109/TSE.2017.2659745
  102. The Open Provenance Model core specification (v1.1). Future Generation Computer Systems 27, 6 (2011), 743–756. https://doi.org/10.1016/j.future.2010.07.005
  103. Data Provenance Model for Internet of Things (IoT) Systems. In Service-Oriented Computing – ICSOC 2016 Workshops. Springer International Publishing, Cham, 85–91.
  104. Cross-Level Sensor Network Simulation with COOJA. In Proceedings. 2006 31st IEEE Conference on Local Computer Networks. 641–648. https://doi.org/10.1109/LCN.2006.322172
  105. Data Provenance in Security and Privacy. ACM Comput. Surv. (apr 2023). https://doi.org/10.1145/3593294
  106. In the shadows we trust: A secure aggregation tolerant watermark for data streams. In 2015 IEEE 16th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM). 1–9. https://doi.org/10.1109/WoWMoM.2015.7158149
  107. Unkyu Park and John Heidemann. 2008. Provenance in Sensornet Republishing. In Provenance and Annotation of Data and Processes: Second International Provenance and Annotation Workshop, IPAW 2008, Salt Lake City, UT, USA, June 17-18, 2008. Revised Selected Papers. Springer-Verlag, Berlin, Heidelberg, 280–292. https://doi.org/10.1007/978-3-540-89965-5_28
  108. Pinocchio: Nearly Practical Verifiable Computation. Commun. ACM 59, 2 (jan 2016), 103–112. https://doi.org/10.1145/2856449
  109. PowerTOSSIM z: Realistic Energy Modelling for Wireless Sensor Network Environments. In Proceedings of the 3nd ACM Workshop on Performance Monitoring and Measurement of Heterogeneous Wireless and Wired Networks (PM2HW2N ’08). Association for Computing Machinery, New York, NY, USA, 35–42. https://doi.org/10.1145/1454630.1454636
  110. Systematic Mapping Studies in Software Engineering. In EASE’08: Proceedings of the 12th international conference on Evaluation and Assessment in Software Engineering (Italy) (EASE’08). BCS Learning & Development Ltd., Swindon, GBR, 68–77. https://doi.org/doi/10.5555/2227115.2227123
  111. S. Porkodi and D. Kesavaraja. 2021. Secure Data Provenance in Internet of Things using Hybrid Attribute based Crypt Technique. Wireless Personal Communications (Feb 2021). https://doi.org/10.1007/s11277-021-08157-0
  112. R Manjunatha Prasad and Shivaprakash Koliwad. 2010. A robust wavelet-based watermarking scheme for copyright protection of digital images. In 2010 Second International conference on Computing, Communication and Networking Technologies. 1–9. https://doi.org/10.1109/ICCCNT.2010.5591586
  113. A mutual agreement signature scheme for secure data provenance. In The 23rd International Conference on Computer Communications and Networks. 726–733. https://cs.iupui.edu/~xzou/Papers/ICCCN14_Digital_Provenance.pdf
  114. Steffen Remus and Chris Biemann. 2013. Three Knowledge-Free Methods for Automatic Lexical Chain Extraction. In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Lucy Vanderwende, Hal Daumé III, and Katrin Kirchhoff (Eds.). Association for Computational Linguistics, Atlanta, Georgia, 989–999. https://aclanthology.org/N13-1119
  115. A Public Dataset of 24-h Multi-Levels Psycho-Physiological Responses in Young Healthy Adults. Data 5, 4 (2020). https://doi.org/10.3390/data5040091
  116. Hadi Sabaa and Brajendra Panda. 2007. Data authentication and provenance management. In 2007 2nd International Conference on Digital Information Management, Vol. 1. 309–314. https://doi.org/10.1109/ICDIM.2007.4444241
  117. ProvLink-IoT: A novel provenance model for Link-Layer Forensics in IoT networks. Forensic Science International: Digital Investigation 46 (2023), 301600. https://doi.org/10.1016/j.fsidi.2023.301600
  118. Security Services Using Blockchains: A State of the Art Survey. IEEE Communications Surveys and Tutorials 21, 1 (2019), 858–880. https://doi.org/10.1109/COMST.2018.2863956
  119. Samsung. 2017. SmartThings IDE. https://graph.api.smartthings.com/
  120. Adi Shamir. 1979. How to Share a Secret. Commun. ACM 22, 11 (nov 1979), 612–613. https://doi.org/10.1145/359168.359176
  121. Demonstrating a Lightweight Data Provenance for Sensor Networks. In Proceedings of the 2012 ACM Conference on Computer and Communications Security (CCS ’12). Association for Computing Machinery, 1022–1024. https://doi.org/10.1145/2382196.2382312
  122. Edge Computing: Vision and Challenges. IEEE Internet of Things Journal 3, 5 (2016), 637–646. https://doi.org/10.1109/JIOT.2016.2579198
  123. Secure Data Provenance in Internet of Things based Networks by Outsourcing Attribute based Signatures and using Bloom Filters. International Journal of Advanced Computer Science and Applications 10, 5 (2019). https://doi.org/10.14569/IJACSA.2019.0100529
  124. BlockTrack-L: A Lightweight Blockchain-based Provenance Message Tracking in IoT. International Journal of Advanced Computer Science and Applications 11, 4 (2020). https://doi.org/10.14569/IJACSA.2020.0110462
  125. Blockchain-Based Data Provenance for the Internet of Things. In Proceedings of the 9th International Conference on the Internet of Things (Bilbao, Spain) (IoT ’19). Association for Computing Machinery, New York, NY, USA, Article 15, 8 pages. https://doi.org/10.1145/3365871.3365886
  126. A secure and extensible blockchain-based data provenance framework for the Internet of Things. Personal and Ubiquitous Computing (16 Jun 2020). https://doi.org/10.1007/s00779-020-01417-z
  127. Wellington Fernandes Silvano and Roderval Marcelino. 2020. Iota Tangle: A cryptocurrency to communicate Internet-of-Things data. Future Gener. Comput. Syst. 112 (2020), 307–319. https://api.semanticscholar.org/CorpusID:219753144
  128. A Survey of Data Provenance in E-Science. SIGMOD Rec. 34, 3 (sep 2005), 31–36. https://doi.org/10.1145/1084805.1084812
  129. SmartThings. Accessed 1 August 2023. SmartThings public GitHub Repo. https://github.com/SmartThingsCommunity/SmartThingsPublic/tree/master/smartapps
  130. A Byzantine Fault-Tolerant Ordering Service for the Hyperledger Fabric Blockchain Platform. In 2018 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN). 51–58. https://doi.org/10.1109/DSN.2018.00018
  131. Introducing Secure Provenance in IoT: Requirements and Challenges. In 2016 International Workshop on Secure Internet of Things (SIoT). 39–46. https://doi.org/10.1109/SIoT.2016.011
  132. Data trustworthiness in IoT. In 2018 International Conference on Information Networking (ICOIN). 414–419. https://doi.org/10.1109/ICOIN.2018.8343151
  133. Provenance-enabled packet path tracing in the RPL-based internet of things. Computer Networks 173 (2020), 107189. https://doi.org/10.1016/j.comnet.2020.107189
  134. A Provenance Based Mechanism to Identify Malicious Packet Dropping Adversaries in Sensor Networks. In 2011 31st International Conference on Distributed Computing Systems Workshops. 332–338. https://doi.org/10.1109/ICDCSW.2011.54
  135. A Lightweight Secure Scheme for Detecting Provenance Forgery and Packet DropAttacks in Wireless Sensor Networks. IEEE Transactions on Dependable and Secure Computing 12, 3 (2015), 256–269. https://doi.org/10.1109/TDSC.2013.44
  136. Secure Provenance Transmission for Streaming Data. IEEE Transactions on Knowledge and Data Engineering 25, 8 (2013), 1890–1903. https://doi.org/10.1109/TKDE.2012.31
  137. A Novel Blockchain-Based IoT Data Provenance Model. In 2022 2nd International Conference on Computer Science and Blockchain (CCSB). 46–52. https://doi.org/10.1109/CCSB58128.2022.00015
  138. Zhaohui Tang and Sye Loong Keoh. 2020. An Efficient Scheme to Secure Data Provenance in Home Area Networks. In 2020 IEEE 3rd 5G World Forum (5GWF). 115–120. https://doi.org/10.1109/5GWF49715.2020.9221402
  139. Dictionary Based Secure Provenance Compression for Wireless Sensor Networks. IEEE Transactions on Parallel and Distributed Systems 27, 2 (2016), 405–418. https://doi.org/10.1109/TPDS.2015.2402156
  140. Provenance for Wireless Sensor Networks: A Survey. Data Science and Engineering 1, 3 (01 Sep 2016), 189–200. https://doi.org/10.1007/s41019-016-0017-x
  141. Fear and Logging in the Internet of Things. In Network and Distributed System Security Symposium. 1–15. https://doi.org/10.14722/NDSS.2018.23282
  142. You Are What You Do: Hunting Stealthy Malware via Data Provenance Analysis. Proceedings 2020 Network and Distributed System Security Symposium (2020). https://www.ndss-symposium.org/wp-content/uploads/2020/02/24167-paper.pdf
  143. Chaining for securing data provenance in distributed information networks, In MILCOM 2012 - 2012 IEEE Military Communications Conference. Proceedings - IEEE Military Communications Conference MILCOM, 1–6. https://doi.org/10.1109/MILCOM.2012.6415609
  144. Y. Richard Wang and Stuart E. Madnick. 1990. A Polygon Model for Heterogeneous Database Systems: The Source Tagging Perspective. In Proceedings of the Sixteenth International Conference on Very Large Databases (Brisbane, Australia). Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 519–533. https://doi.org/doi/10.5555/94362.94604
  145. Gavin Wood. 2014. Ethereum: A secure decentralised generalised transaction ledger. Journal of Computer and Communications 151 (2014), 1–32. https://api.semanticscholar.org/CorpusID:261284440
  146. Pagoda: A Hybrid Approach to Enable Efficient Real-Time Provenance Based Intrusion Detection in Big Data Environments. IEEE Transactions on Dependable and Secure Computing 17, 6 (2020), 1283–1296. https://doi.org/10.1109/TDSC.2018.2867595
  147. Unifying intrusion detection and forensic analysis via provenance awareness. Future Generation Computer Systems 61 (2016), 26–36. https://doi.org/10.1016/j.future.2016.02.005
  148. P-Gaussian: Provenance-Based Gaussian Distribution for Detecting Intrusion Behavior Variants Using High Efficient and Real Time Memory Databases. IEEE Transactions on Dependable and Secure Computing 18, 6 (2021), 2658–2674. https://doi.org/10.1109/TDSC.2019.2960353
  149. Compact Provenance Scheme Through Packet Path Index Differences in WSNs. IEEE Transactions on Information Forensics and Security 17 (2022), 3511–3524. https://doi.org/10.1109/TIFS.2022.3207897
  150. Qinbao Xu and Changda Wang. 2022. Stepwise Refinement Provenance Scheme for Wireless Sensor Networks. IEEE Internet of Things Journal 9, 13 (2022), 11126–11140. https://doi.org/10.1109/JIOT.2021.3127496
  151. Provenance Compression Using Packet-Path-Index Differences in Wireless Sensor Networks. In 2019 15th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN). 200–205. https://doi.org/10.1109/MSN48538.2019.00047
  152. Context-Aware Verifiable Cloud Computing. IEEE Access 5 (2017), 2211–2227. https://doi.org/10.1109/ACCESS.2017.2666839
  153. Fei Yin and Zhiqiang Fu. 2022. A Data Provenance Scheme Based on Blockchain for Internet of Things. In 2022 2nd International Conference on Computer Science and Blockchain (CCSB). 42–45. https://doi.org/10.1109/CCSB58128.2022.00014
  154. A Survey of Verifiable Computation. Mobile Networks and Applications 22, 3 (01 Jun 2017), 438–453. https://doi.org/10.1007/s11036-017-0872-3
  155. Verifiable outsourced computation over encrypted data. Information Sciences 479 (2019), 372–385. https://doi.org/10.1016/j.ins.2018.12.022
  156. Trustworthy data: A survey, taxonomy and future trends of secure provenance schemes. Journal of Network and Computer Applications 94 (2017), 50–68. https://doi.org/10.1016/j.jnca.2017.06.003
  157. SECProv: Trustworthy and Efficient Provenance Management in the Cloud. In IEEE INFOCOM 2018 - IEEE Conference on Computer Communications. 1241–1249. https://doi.org/10.1109/INFOCOM.2018.8485824
  158. A Blockchain-Based Scheme for Secure Data Provenance in Wireless Sensor Networks. In 2018 14th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN). 13–18. https://doi.org/10.1109/MSN.2018.00009
  159. Secure and Optimized Load Balancing for Multitier IoT and Edge-Cloud Computing Systems. IEEE Internet of Things Journal 8, 10 (2021), 8119–8132. https://doi.org/10.1109/JIOT.2020.3042433
  160. Provenance-Based Intrusion Detection Systems: A Survey. ACM Comput. Surv. 55, 7, Article 135 (dec 2022), 36 pages. https://doi.org/10.1145/3539605
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Authors (3)
  1. Omair Faraj (2 papers)
  2. Joaquin Garcia-Alfaro (32 papers)
  3. David Megias (2 papers)

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