Abstract
Cloud computing is a newly emerging technology where storage, computation and services are extensively shared among a large number of users through virtualization and distributed computing. This technology makes the process of detecting the physical location or ownership of a particular piece of data even more complicated. As a result, improvements in data provenance techniques became necessary. Provenance refers to the record describing the origin and other historical information about a piece of data. An advanced data provenance system will give forensic investigators a transparent idea about the data's lineage, and help to resolve disputes over controversial pieces of data by providing digital evidence. In this paper, the challenges of cloud architecture are identified, how this affects the existing forensic analysis and provenance techniques is discussed, and a model for efficient provenance collection and forensic analysis is proposed.
References
Abbadi, I. M., Lyle, J., et al. (2011). Challenges for provenance in cloud computing. In Tapp.
Alqahtany, S., Clarke, N., Furnell, S., & Reich, C. (2015). Cloud forensics: a review of challenges, solutions and open problems. In Cloud computing, 2015 international conference on (pp. 1{9).
Amazon Web Services. (2008, September). Building greptheweb in the cloud, part 1: Cloud architectures. (http://developer.amazonwebservices.com/ connect/ entry.jspa?externalID=1632 [September 30 2016])
Bohn, R. B., Messina, J., Liu, F., Tong, J., & Mao, J. (2011). Nist cloud computing reference architecture. In Services (services), 2011 ieee world congress on (pp. 594{596).
Borthakur, D., et al. (2008). Hdfs architecture guide. Hadoop Apache Project, 53 .
Buneman, P., Khanna, S., & Wang-Chiew, T. (2001). Why and where: A characterization of data provenance. In International conference on database theory (pp. 316{330).
Carrier, B., Spaford, E. H., et al. (2003). Getting physical with the digital investigation process. International Journal of digital evidence, 2 (2), 1{20.
Chung, M., & Hermans, J. (2010). From hype to future: Kpmg's 2010 cloud computing survey. KPMG, 8{28.
Cloud Security Alliance. (2011, September). Cloud controls matrix v1.2 - cloud controls matrix working group. (https://cloudsecurityalliance.org/ download/ cloud-controls-matrix-v1-2/)
Ghemawat, S., Gobio , H., & Leung, S.-T. (2003). The google system (Vol. 37) (No. 5). ACM.
Hashizume, K., Rosado, D. G., Fernandez-Medina, E., & Fernandez, E. B. (2013). An analysis of security issues for cloud computing. Journal of internet services and applications, 4 (1), 5.
Imran, M., & Hlavacs, H. (2013). Layering of the provenance data for cloud computing. In International conference on grid and pervasive computing (pp. 48{58).
Jansen, W., & Grance, T. (2011). Sp 800-144. guidelines on security and privacy in public cloud computing.
Kandukuri, B. R., Rakshit, A., et al. (2009). Cloud security issues. In Services computing, 2009. scc'09. ieee international conference on (pp. 517{520).
Katilu, V. M., Franqueira, V. N., &Angelopoulou, O. (2015). Challenges of data provenance for cloud forensic investigations. In Availability, reliability and security (ares), 2015 10th international conference on (pp. 312{317).
Kent, K., Chevalier, S., Grance, T., & Dang, H. (2006). Guide to integrating forensic techniques into incident response. NIST Special Publication, 10 , 800{86.
Ko, R. K., Jagadpramana, P., & Lee, B. S. (2011). Flogger: A logger for monitoring access and transfers within cloud computing environments. In Trust, security and privacy in computing and communications (trustcom), 2011 ieee 10th international conference on (pp. 765{771).
Li, J., Chen, X., Huang, Q., & Wong, D. S. (2014). Digital provenance: Enabling secure data forensics in cloud computing. Future Generation Computer Systems, 37 , 259{266.
Lu, R., Lin, X., Liang, X., & Shen, X. S. (2010). Secure provenance: the essential of bread and butter of data forensics in cloud computing. In Proceedings of the 5th acm symposium on information, computer and communications security (pp. 282{292).
Market Research Media. (2016, September). Global cloud computing market forecast 2015-2020. (https://www.marketresearchmedia.com/ ?p=839)
Marty, R. (2011). Cloud application logging for forensics. In Proceedings of the 2011 acm symposium on applied computing (pp. 178{184).
Mell, P., Grance, T., et al. (2011). The of cloud computing.
Muniswamy-Reddy, K.-K. (2006). Deciding how to store provenance.
Muniswamy-Reddy, K.-K., Holland, D. A., Braun, U., & Seltzer, M. I. (2006). Provenance-aware storage systems. In Usenix annual technical conference, general track (pp. 43{56).
Muniswamy-Reddy, K.-K., Macko, P., & Seltzer, M. I. (2010). Provenance for the cloud. In Fast (Vol. 10, pp. 15{14).
O'shaughnessy, S., & Keane, A. (2013). Impact of cloud computing on digital forensic investigations. international conference on digital forensics (pp. 291{303).
Ren, K., Wang, C., & Wang, Q. (2012). Security challenges for the public cloud. IEEE Internet Computing, 16 (1), 69{73.
Ruan, K., & Carthy, J. (2012). Cloud computing reference architecture and its forensic implications: A preliminary analysis. In International conference on digital forensics and cyber crime (pp. 1{21).
Sakka, M. A., Defude, B., & Tellez, J. (2010). Document provenance in the cloud: constraints and challenges. In Meeting of the european network of universities and companies in information and communication engineering (pp. 107{117).\
Simmhan, Y. L., Plale, B., & Gannon, D. (2005). A survey of data provenance in e-science. ACM Sigmod Record,34 (3), 31{36.
Suen, C. H., Ko, R. K., Tan, Y. S., Jagadpramana, P., & Lee, B. S. (2013). S2logger: End-to-end data tracking mechanism for cloud data provenance. In Trust, security and privacy in computing and communications (trustcom), 2013 12th ieee international conference on (pp. 594{602).
Sultana, S., & Bertino, E. (2013). A provenance system. In Proceedings of the third acm conference on data and application security and privacy (pp. 153{156).
Taha, M. M. B., Chaisiri, S., & Ko, R. K. (2015). Trusted tamper-evident data provenance. In Trustcom/bigdatase/ispa, 2015 ieee (Vol. 1, pp. 646{653).
Tan, W. C. (2004). Research problems in data provenance. IEEE Data Eng. Bull., 27 (4), 45{52.
Tan, Y. S., Ko, R. K., & Holmes, G. (2013). Security and data accountability in distributed systems: A provenance survey. In High performance computing and communications & 2013 ieee international conference on embedded and ubiquitous computing (hpcc euc), 2013 ieee 10th international conference on (pp. 1571{1578).
Trenwith, P., & Venter, H. (2015). Locating and tracking digital objects in the cloud. In international conference on digital forensics (pp. 287{301).
Trenwith, P. M., & Venter, H. S. (2014). A digital forensic model for providing better data provenance in the cloud. In Information security for south africa (issa), 2014 (pp. 1{6).
Voorsluys, W., Broberg, J., & Buyya, R. (2011). Introduction to cloud computing. Cloud computing: Principles and paradigms, 1{41.
Wells, D., Greisen, E., & Harten, R. (1981). Fits-a exible image transport system. Astronomy and Astrophysics Supplement Series, 44 , 363.
Zawoad, S., Hasan, R., & Skjellum, A. (2015). Ocf: an open cloud forensics model for reliable digital forensics. In Cloud computing (cloud), 2015 ieee 8th international conference on (pp. 437{444).
Zhang, O. Q., Kirchberg, M., Ko, R. K., & Lee, B. S. (2011). How to track your data: The case for cloud computing provenance. In Cloud computing technology and science (cloudcom), 2011 ieee third international conference on (pp. 446{453).
Zhao, J., Bizer, C., Gil, Y., Missier, P., & Sahoo, S. (2010). Provenance requirements for the next version of rdf. In W3c workshop rdf next steps.
Recommended Citation
Haque, Shariful and Atkison, Travis
(2018)
"A Forensic Enabled Data Provenance Model for Public Cloud,"
Journal of Digital Forensics, Security and Law: Vol. 13
, Article 7.
DOI: https://doi.org/10.15394/jdfsl.2018.1570
Available at:
https://commons.erau.edu/jdfsl/vol13/iss3/7
