Enriching the Open Provenance Model for a Privacy-Aware Provenance Management

Yıl 2021, , 144 - 149, 01.12.2021

Dilek Yılmazer Demirel , Özgü Can

https://doi.org/10.31590/ejosat.1023420

Öz

Today, the total amount of data that is generated, copied, and stored are increasing rapidly. Thereupon, the trustworthiness of the data source and the quality of data have significant importance for an effective data analysis. Therefore, it is critical to improve accountability for the quality of data. For this purpose, provenance information is used to provide the quality of data. Provenance information ensures the reliability and quality of data. Data provenance is a form of metadata to describe the life cycle of a data. Therefore, provenance information maintains the history of the data by describing how data are derived. The Open Provenance Model (OPM) aims to meet the requirements of a provenance model. For this purpose, OPM defines a core set of rules. Thus, OPM provides provenance interoperability. In this study, OPM is enhanced to provide a Privacy-Aware Provenance Management (PAPM) model. The goal of the PAPM model is to use provenance information in order to protect data from unwanted access and detect security violations. Therefore, PAPM uses provenance information to protect data privacy. Since the proposed PAPM model is domain-independent, it can be integrated into any interested domain to preserve privacy and ensure data security.

Anahtar Kelimeler

Provenance, Open Provenance Model, Privacy, Data Security, Knowledge Engineering, Semantic Web.

Mahremiyet-Farkında Bir Köken Yönetimi için Açık Köken Modelinin Zenginleştirilmesi

Öz

Günümüzde üretilen, kopyalanan ve depolanan toplam veri miktarı hızla artmaktadır. Bunun sonucu olarak, etkin bir veri analizi için veri kaynağının güvenilirliği ve verinin kalitesi büyük önem taşımaktadır. Bu nedenle, veri kalitesi için izlenebilirliği arttırmak çok önemlidir. Bu amaçla, veri kalitesini sağlamak için köken bilgisi kullanılmaktadır. Köken bilgisi, verilerin güvenilirliğini ve kalitesini sağlamaktadır. Veri kökeni, verinin yaşam döngüsünü tanımlayan bir meta veri biçimidir. Bu nedenle, köken bilgisi, verilerin nasıl türetildiğini açıklayarak verilerin geçmişini korumaktadır. Açık Köken Modeli (OPM), bir köken modelinin gereksinimlerini karşılamayı hedefklemektedir. Bu amaçla, OPM temel bir kurallar kümesi tanımlamaktadır. Böylelikle, OPM köken birlikte çalışabilirliğini sağlamaktadır. Bu çalışmada, Gizlilik-Farkında bir Köken Yönetimi (PAPM) modeli sağlamak için OPM genişletilmiştir. PAPM modelinin amacı, verileri istenmeyen erişimlerden korumak ve güvenlik ihlallerini tespit etmek için köken bilgisini kullanmaktır. Bu nedenle PAPM, veri mahremiyetini korumak için köken bilgisini kullanmaktadır. Önerilen PAPM modeli etki alanından bağımsız olduğundan, mahremiyeti korumak ve veri güvenliğini sağlamak için herhangi bir etki alanına entegre edilebilecektir.

Anahtar Kelimeler

Köken, Açık Köken Modeli, Mahremiyet, Veri Güvenliği, Bilgi Mühendisliği, Anlamsal Web.

Kaynakça

• Burgess, L.C. (2016). Provenance in Digital Libraries: Source, Context, Value and Trust. In: Lemieux V. (eds) Building Trust inInformation. Springer Proceedings in Business and Economics, pp. 81-91. Springer, Cham.
• Butt, A.S, & Fitch, P. (2021). A provenance model for control-flow driven scientific workflows. Data & Knowledge Engineering, 131-132, 101877.
• Butt A.S., & Fitch P. (2020). ProvONE+: A Provenance Model for Scientific Workflows. In: Huang Z., Beek W., Wang H., Zhou R., Zhang Y. (eds) Web Information Systems Engineering – WISE 2020. Lecture Notes in Computer Science, Vol 12343, pp. 431-444. Springer, Cham.
• Can, O., & Yilmazer, D. (2014). A Privacy-Aware Semantic Model for Provenance Management. In: Closs S., Studer R., Garoufallou E., Sicilia MA. (eds) Metadata and Semantics Research (MTSR 2014). CCIS Vol 478, pp. 162-169. Springer, Cham.
• Can, O., & Yilmazer, D. (2020). A novel approach to provenance management for privacy preservation. Journal of Information Science, 46(2):147-160.
• Can, O., & Yilmazer, D. (2020). Improving privacy in health care with an ontology-based provenance management system. Expert Systems, 37(1), 12427.
• Cheahi Y.W., & Plale, B. (2014). Provenance quality assessment methodology and framework. ACM Journal of Data and Information Quality, 5(3), Article 9.
• Davidson, S.B., & Freire, J. (2008). Provenance and scientific workflows: challenges and opportunities. In: Proceedings of the 2008 ACM SIGMOD international conference on management of data (SIGMOD’08), pp. 1345-1350.
• Dublin Core Terms, https://www.dublincore.org/specifications/dublin-core. Last accessed 09 Oct 2021.
• Elkhodr, M., & Mufti, Z.B. (2019). On the challenges of data provenance in the Internet of Things. International Journal of Wireless & Mobile Networks (IJWMN), 11(3):43-52.
• Garrard, R., & Fielke, S. (2020). Blockchain for trustworthy provenances: A case study in the Australian aquaculture industry. Technology in Society, 62, 101298.
• Golbeck, J., & Hendler, J. (2008). A semantic web approach to the provenance challenge. Concurrency and Computation: Practice and Experience, 20(5): 431-439.
• Kwasnikowska, N., Moreau, L., & Van Den Bussche, J. (2015). A Formal Account of the Open Provenance Model. ACM Transactions on the Web, 9:2, Article 10.
• Liang, X., et al. (2017). ProvChain: A Blockchain-based Data Provenance Architecture in Cloud Environment with Enhanced Privacy and Availability. In: 17th IEEE/ACM Int. Symposium on Cluster, Cloud and Grid Computing (CCGRID), pp. 468-477.
• Miles, S., Moreau, L., & Futrelle, J. (2009). OPM Profile for Dublin Core Terms (Draft). https://nms.kcl.ac.uk/luc.moreau/papers/dc-opm09.pdf. Last accessed 09 Oct 2021. Moreau, L., Clifford, B., Freire, J., et al. (2011). The Open Provenance Model core specification (v1.1). Future Generation Computer Systems, 27(6): 743-756.
• Moreau L., Freire J., Futrelle J., McGrath R.E., Myers J., & Paulson P. (2008). The Open Provenance Model: An Overview. In: Freire J., et al. (eds) Provenance and Annotation of Data and Processes. IPAW 2008. LNCS, Vol 5272. Springer, Berlin, Heidelberg.
• Omitola, T., Gibbins, N., & Shadbolt, N. (2010). Provenance in Linked Data Integration. Future Internet Assembly.
• OPM Tutorial. Interoperability. https://openprovenance.org/opm/tutorial/slides/6-interoperability.pptx. Last accessed 09 Oct 2021.
• Phua, T.W., & Ko, R.K.L. (2018). Data Provenance for Big Data Security and Accountability. In: Sakr S., Zomaya A. (eds) Encyclopedia of Big Data Technologies. Springer, Cham. Provenance. https://www.lexico.com/definition/provenance. Last accessed 09 Oct 2021.
• Suhail, S., Hussain, R., Khan, A., & Seon Hong, C. (2020). Orchestrating product provenance story: When IOTA eco system meets electronics supply chain space. Computers in Industry, 123, 103334.
• Tan, A.Y.S, et al. (2015). Provenance for cloud data accountability. The Cloud Security Ecosystem Technical, Legal, Business and Management Issues. 1st Edn. Chapter 8, pp. 171--185. Syngress, MA, USA.
• Tan, Y.S., Ko, R.K.L., & Holmes, G. (2013). Security and Data Accountability in Distributed Systems: A Provenance Survey. In: IEEE 10th International Conference on High Performance Computing and Communications \& 2013 IEEE International Conference on Embedded and Ubiquitous Computing, pp. 1571-1578.
• Tomazela, B., Hara, C.S., Ciferri, R.R., & de Aguiar Ciferri, C.D. (2013). Empowering integration processes with data provenance. Data & Knowledge Engineering, 86:102-123.