Securing Big Data Provenance for Auditors: The Big Data Provenance Black Box as Reliable Evidence

ABSTRACT: The purpose of this article is to highlight a main issue regarding reliable audit evidence derived from Big Data—that of secure data provenance. Traditionally, audit evidence external to the client has been regarded as superior to other forms of evidence. However, regarding external “messy” Big Data sources that may be material to aspects of the audit, these sources may lack provenance and verifiability. That is, the origins of the data may be unclear and its log files incomplete. According to the standards, such evidence should be considered as less reliable for audit evidence. External auditors, as outsiders of the client, should be able to reproduce the data lifecycle or transaction path, which may not be possible in an electronic environment with incomplete provenance. Furthermore, this mapping or provenance of the data origins and history should be securely maintained so that it cannot be thwarted. This need for secure data provenance has been largely ignored by the business community in it...

[1]  Luc Moreau,et al.  Securing Provenance-Based Audits , 2010, IPAW.

[2]  Hammerbacher Jeff Information Platforms and the Rise of the Data Scientist , 2016 .

[3]  Bertram Ludäscher,et al.  Provenance in Scientific Workflow Systems , 2007, IEEE Data Eng. Bull..

[4]  Kevin R. B. Butler,et al.  Towards secure provenance-based access control in cloud environments , 2013, CODASPY.

[5]  Christoph Bier How Usage Control and Provenance Tracking Get Together - A Data Protection Perspective , 2013, 2013 IEEE Security and Privacy Workshops.

[6]  Yogesh L. Simmhan,et al.  A survey of data provenance in e-science , 2005, SGMD.

[7]  Juliana Freire,et al.  Provenance and scientific workflows: challenges and opportunities , 2008, SIGMOD Conference.

[8]  James Frew,et al.  Lineage retrieval for scientific data processing: a survey , 2005, CSUR.

[9]  Beth Plale,et al.  Provenance analysis: Towards quality provenance , 2012, 2012 IEEE 8th International Conference on E-Science.

[10]  Mark Taylor,et al.  Digital evidence in cloud computing systems , 2010, Comput. Law Secur. Rev..

[11]  Margo I. Seltzer,et al.  Layering in Provenance Systems , 2009, USENIX Annual Technical Conference.

[12]  Rajkumar Buyya,et al.  Big Data computing and clouds: Trends and future directions , 2013, J. Parallel Distributed Comput..

[13]  James A. Hendler,et al.  Information accountability , 2008, CACM.

[14]  Margo I. Seltzer,et al.  Provenance for the Cloud , 2010, FAST.

[15]  Rolf Oppliger,et al.  Digital Evidence: Dream and Reality , 2003, IEEE Secur. Priv..

[16]  Marianne Winslett,et al.  The Case of the Fake Picasso: Preventing History Forgery with Secure Provenance , 2009, FAST.

[17]  Fabio Kon,et al.  A comprehensive view of Hadoop research - A systematic literature review , 2014, J. Netw. Comput. Appl..

[18]  Jimmy J. Lin,et al.  Scaling big data mining infrastructure: the twitter experience , 2013, SKDD.

[19]  Subrata Acharya,et al.  Towards a trusted HDFS storage platform: Mitigating threats to Hadoop infrastructures using hardware-accelerated encryption with TPM-rooted key protection , 2014, J. Inf. Secur. Appl..

[20]  Vladimiro Sassone,et al.  A Formal Model of Provenance in Distributed Systems , 2009, Workshop on the Theory and Practice of Provenance.

[21]  Randy H. Katz,et al.  A view of cloud computing , 2010, CACM.

[22]  Melissa Dark,et al.  Toward a Data Spillage Prevention Process in Hadoop using Data Provenance , 2015, CLHS '15.

[23]  Zhiyong Peng,et al.  From Big Data to Big Data Mining: Challenges, Issues, and Opportunities , 2013, DASFAA Workshops.

[24]  Jennifer Widom,et al.  Provenance for Generalized Map and Reduce Workflows , 2011, CIDR.

[25]  Miklos A. Vasarhelyi,et al.  Big Data and Audit Evidence , 2015 .

[26]  Bruno Defude,et al.  Document Provenance in the Cloud: Constraints and Challenges , 2010, EUNICE.

[27]  James Cheney,et al.  Provenance in Databases: Why, How, and Where , 2009, Found. Trends Databases.

[28]  Jennifer Widom,et al.  Panda: A System for Provenance and Data , 2010, IEEE Data Eng. Bull..

[29]  Susan B. Davidson,et al.  Addressing the provenance challenge using ZOOM , 2008 .

[30]  Youngseok Lee,et al.  Secure Hadoop with Encrypted HDFS , 2013, GPC.

[31]  Boris Glavic Big Data Provenance: Challenges and Implications for Benchmarking , 2012, WBDB.

[32]  Limin Jia,et al.  Evidence-Based Audit , 2008, 2008 21st IEEE Computer Security Foundations Symposium.

[33]  Juan Zhang,et al.  Toward Effective Big Data Analysis in Continuous Auditing , 2015 .

[34]  Sanjay Ghemawat,et al.  MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.

[35]  Sanjeev Khanna,et al.  Differencing Provenance in Scientific Workflows , 2009, 2009 IEEE 25th International Conference on Data Engineering.

[36]  Barbara Poblete,et al.  Information credibility on twitter , 2011, WWW.

[37]  Klaus R. Dittrich,et al.  Data Provenance: A Categorization of Existing Approaches , 2007, BTW.

[38]  Wang Chiew Tan Provenance in Databases: Past, Current, and Future , 2007, IEEE Data Eng. Bull..

[39]  Raymond A. Paul,et al.  Data provenance in SOA: security, reliability, and integrity , 2007, Service Oriented Computing and Applications.

[40]  Margo I. Seltzer,et al.  Securing Provenance , 2008, HotSec.

[41]  Luc Moreau,et al.  The Open Provenance Model: An Overview , 2008, IPAW.

[42]  Sanjeev Khanna,et al.  Data Provenance: Some Basic Issues , 2000, FSTTCS.

[43]  Miklos A. Vasarhelyi,et al.  Process Mining of Event Logs in Auditing: Opportunities and Challenges , 2010 .

[44]  Jianwu Wang,et al.  Provenance for MapReduce-based data-intensive workflows , 2011, WORKS '11.

[45]  Peter Buneman,et al.  Provenance in databases , 2009, SIGMOD '07.

[46]  Kevin C. Moffitt,et al.  How Big Data Will Change Accounting , 2015 .

[47]  Anne E. Trefethen,et al.  The Data Deluge: An e-Science Perspective , 2003 .

[48]  Miklos A. Vasarhelyi,et al.  Restoring auditor credibility: tertiary monitoring and logging of continuous assurance systems , 2004, Int. J. Account. Inf. Syst..

[49]  Rafael Accorsi,et al.  Safe-Keeping Digital Evidence with Secure Logging Protocols: State of the Art and Challenges , 2009, 2009 Fifth International Conference on IT Security Incident Management and IT Forensics.

[50]  Jennifer Widom,et al.  Lineage tracing for general data warehouse transformations , 2003, The VLDB Journal.

[51]  Yogesh L. Simmhan,et al.  A survey of data provenance techniques , 2005 .

[52]  Miklos A. Vasarhelyi,et al.  Feasibility and Economics of Continuous Assurance , 2002 .

[53]  Roberto Di Pietro,et al.  Fame for sale: Efficient detection of fake Twitter followers , 2015, Decis. Support Syst..

[54]  Wang Chiew Tan,et al.  Research Problems in Data Provenance , 2004, IEEE Data Eng. Bull..

[55]  Paul T. Groth,et al.  The provenance of electronic data , 2008, CACM.

[56]  Jennifer Widom,et al.  RAMP: A System for Capturing and Tracing Provenance in MapReduce Workflows , 2011, Proc. VLDB Endow..

[57]  Jignesh M. Patel,et al.  Big data and its technical challenges , 2014, CACM.

[58]  James Frew,et al.  Automatic capture and reconstruction of computational provenance , 2008 .

[59]  Dennis Shasha,et al.  Improving Data Cleaning Quality Using a Data Lineage Facility , 2001, DMDW.

[60]  Anna Cinzia Squicciarini,et al.  Towards Provenance-Based Anomaly Detection in MapReduce , 2015, 2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing.

[61]  Roy D. Sleator,et al.  'Big data', Hadoop and cloud computing in genomics , 2013, J. Biomed. Informatics.

[62]  Sanjeev Khanna,et al.  Why and Where: A Characterization of Data Provenance , 2001, ICDT.

[63]  Sushil Jajodia,et al.  Detecting Automation of Twitter Accounts: Are You a Human, Bot, or Cyborg? , 2012, IEEE Transactions on Dependable and Secure Computing.

[64]  Rafael Accorsi,et al.  On the Relationship of Privacy and Secure Remote Logging in Dynamic Systems , 2006, SEC.

[65]  Daniel W. Margo,et al.  Using Provenance to Extract Semantic File Attributes , 2010, TaPP.

[66]  Ian Foster,et al.  Special Issue: The First Provenance Challenge , 2008 .

[67]  Leysia Palen,et al.  Twitter adoption and use in mass convergence and emergency events , 2009 .

[68]  D. Richard Kuhn,et al.  Role-Based Access Controls , 2009, ArXiv.

[69]  Johan Bollen,et al.  Twitter mood predicts the stock market , 2010, J. Comput. Sci..

[70]  Devarshi Ghoshal,et al.  Provenance from log files: a BigData problem , 2013, EDBT '13.

[71]  R. Elliott,et al.  Twenty-First Century Assurance , 2002 .

[72]  Paul T. Groth,et al.  Looking Inside the Black-Box: Capturing Data Provenance Using Dynamic Instrumentation , 2014, IPAW.

[73]  Paul Zikopoulos,et al.  Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data , 2011 .

[74]  Marianne Winslett,et al.  Towards a Secure and Efficient System for End-to-End Provenance , 2010, TaPP.

[75]  Beth Plale,et al.  Big Data Provenance Analysis and Visualization , 2015, 2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing.

[76]  Andy Hopper,et al.  HadoopProv: Towards Provenance as a First Class Citizen in MapReduce , 2013, TaPP.

[77]  Cláudio T. Silva,et al.  Provenance for Computational Tasks: A Survey , 2008, Computing in Science & Engineering.

[78]  Margo I. Seltzer,et al.  Provenance-Aware Storage Systems , 2006, USENIX ATC, General Track.

[79]  Cláudio T. Silva,et al.  Tackling the Provenance Challenge one layer at a time , 2008 .

[80]  Rajeev Agrawal,et al.  A layer based architecture for provenance in big data , 2014, BigData.

[81]  Charu C. Aggarwal,et al.  Trio A System for Data Uncertainty and Lineage , 2009 .

[82]  Jerald Hughes,et al.  ACHIEVING SARBANES-OXLEY COMPLIANCE WITH XBRL-BASED ERP AND CONTINUOUS AUDITING , 2007 .

[83]  Akhil Mittal Trustworthiness of Big Data , 2013 .

[84]  P. Caster,et al.  Technology Changes the Form and Competence of Audit Evidence , 2007 .

[85]  Kees M. van Hee,et al.  Auditing 2.0: Using Process Mining to Support Tomorrow's Auditor , 2010, Computer.

[86]  Dawn Xiaodong Song,et al.  Suspended accounts in retrospect: an analysis of twitter spam , 2011, IMC '11.

[87]  Viktor Mayer-Schönberger,et al.  The Rise of Big Data: How It’s Changing the Way We Think about the World , 2014 .