Modifying Transactional Databases to Hide Sensitive Association Rules

References

• Acquisto G, Domingo-Ferrer J, Kikiras P, Torra V, de Montjoye Y, Bourka A (2015) Privacy by design in big data: An overview of privacy enhancing technologies in the era of big data analytics. ENISA report, European Union Agency for Cybersecurity, Attiki, Greece.Google Scholar
• Adomavicius G, Tuzhilin A (2001) Expert-driven validation of rule-based user models in personalization applications. Data Mining Knowledge Discovery 5(1–2):33–58.Crossref, Google Scholar
• Adomavicius G, Tuzhilin A (2007) Measuring the bullwhip effect: Discrepancy and alignment between information and material flows. INFORMS J. Comput. 19(2):185–200.Link, Google Scholar
• Afshari M, Dehkordi M, Akbari M (2016) Association rule hiding using cuckoo optimization algorithm. Expert Systems Appl. 64:340–351.Crossref, Google Scholar
• Agrawal R, Srikant R (1994) Fast algorithms for mining association rules in large databases. Bocca J, Jarke M, Zaniolo C, eds. Proc. 20th Internat. Conf. Very Large Data Bases (Morgan Kaufmann, San Francisco), 487–499.Google Scholar
• Alaimo D (2013) CGT/RIS retailer/supplier shared data study: A supplement to Consumer Goods Technology and RIS News. Report, RSi Retail Solutions, San Jose, CA.Google Scholar
• Askham N (2014) Is data governance the same as data quality? Experian (blog), August, https://www.experian.co.uk/blogs/latest-thinking/data-and-innovation/is-data-governance-the-same-as-data-quality/.Google Scholar
• Askuity (2018) 2018 POS data study. https://www.askuity.com/wp-content/uploads/2018/01/2018-POS-Data-Study.pdf.Google Scholar
• Atallah M, Bertino E, Elmagarmid A, Ibrahim M, Verykios V 1999) Disclosure limitation of sensitive rules. Scheuermann P, ed. Proc. 1999 Workshop Knowledge Data Engrg. Exchange (IEEE Computer Society, Los Alamitos, CA), 45–52.Google Scholar
• Aviv Y (2002) Gaining benefits from joint forecasting and replenishment processes: The case of auto-correlated demand. Manufacturing Service Oper. Management 4(1):55–74.Link, Google Scholar
• Aviv Y (2007) On the benefits of collaborative forecasting partnerships between retailers and manufacturers. Management Sci. 53(5):777–794.Link, Google Scholar
• Baesens B (2018) Improving data quality using data governance. Big Data Quart. 4(1):43–44.Google Scholar
• Beduhn P (2009) Elephants in your haystack: Manufacturers take their first crack at mining point-of-sale data. Teradata Magazine 9(2).Google Scholar
• Carr R, Doddi S, Konjevod G, Marathe M (2000) On the red-blue set cover problem. Randall D, ed. Proc. 11th Annual ACM-SIAM Sympos. Discrete Algorithms (ACM, New York), 345–353.Google Scholar
• Chen F, Drezner Z, Ryan J, Simchi-Levi D (2000) Privacy and big data: Scalable approaches to sanitize large transactional databases for sharing. Management Sci. 46(3):436–443.Link, Google Scholar
• Cheng P, Lin C-W, Pan J-S (2015) Use HypE to hide association rules by adding items. PLoS One 10(6):e0127834.Crossref, Google Scholar
• Cheng P, Roddick JF, Chu S-C, Lin C-W (2016) Privacy preservation through a greedy, distortion-based rule-hiding method. Appl. Intelligence 44(2):295–306.Crossref, Google Scholar
• Croson R, Donohue K (2003) Impact of POS data sharing on supply chain management: An experimental study. Production Oper. Management 12(1):1–11.Crossref, Google Scholar
• Czyzyk J, Mesnier M, Moré J (1998) The NEOS server. IEEE J. Computational Sci. Engrg. 5(3):68–75.Crossref, Google Scholar
• Dasseni E, Verykios V, Elmagarmid A, Bertino E (2001) Hiding association rules by using confidence and support. Moskowitz IS, ed. Inform. Hiding: Proc. 4th Internat. Inform. Hiding Workshop, Pittsburgh, PA (Springer, Berlin), 382–396.Google Scholar
• DeLallo D, Tennison J (2020) How to make the most of AI? Open up and share data. McKinsey on AI podcast (June 9), 18:19, https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/how-to-make-the-most-of-ai-open-up-and-share-data.Google Scholar
• Dolan E (2001) The NEOS Server 4.0 administrative guide. Technical Report ANL/MCS-TM-250, Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL.Google Scholar
• du Mars R (2012) Data quality process needs all hands on deck. TechTarget (June 12), https://searchdatamanagement.techtarget.com/feature/Data-quality-process-needs-all-hands-on-deck.Google Scholar
• Garfinkel R, Gopal R, Goes P (2002) Privacy protection of binary confidential data against deterministic, stochastic, and insider threat. Management Sci. 48(6):749–764.Link, Google Scholar
• Gkoulalas-Divanis A, Verykios V (2006) An integer programming approach for frequent itemset hiding. Proc. 15th ACM Internat. Conf. Inform. Knowledge Management (ACM, New York), 748–757.Crossref, Google Scholar
• Gopalan N, Murthy T (2019) Association rule hiding using chemical reaction optimization. Bansal J, Das K, Nagar A, Deep K, Ojha A, eds. Soft Computing for Problem Solving (Springer Singapore, Singapore), 249–255.Crossref, Google Scholar
• Gropp W, Moré J (1997) Optimization environments and the NEOS server. Buhmann M, Iserles A, eds. Approximation Theory and Optimization: Tributes to M. J. D. Powell (Cambridge University Press, Cambridge, UK), 167–182.Google Scholar
• Guillet F, Hamilton R (2007) Quality Measures in Data Mining. Studies in Computational Intelligence, Vol. 43 (Springer-Verlag, Berlin).Google Scholar
• Hankinson S (2016) The tipping point: Financial services and data governance. Collibra (blog), September 30, https://www.collibra.com/blog/the-tipping-point-financial-services-and-data-governance.Google Scholar
• Hao J, Menon S, Sarkar S (2007) Preserving privacy when sharing distributed transactional data. 17th Annual Workshop Inform. Tech. Systems, Montreal, Canada.Google Scholar
• IBM (2018) IBM ILOG CPLEX 12.8 User’s Manual (IBM Corporation, Armonk, NY).Google Scholar
• Ivanov K (1972) Quality-control of information: On the concept of accuracy of information in data banks and in management information systems. PhD thesis, University of Stockholm/Royal Institute of Technology, Stockholm.Google Scholar
• Klemettinen M, Mannila H, Ronkainen P, Toivonen H, Verkamo A (1994) Finding interesting rules from large sets of discovered association rules. Adam NR, Bhargava BK, Yesha Y, eds. Proc. Third Internat. Conf. Inform. Knowledge Management(ACM, New York), 401–407.Google Scholar
• Konzak L (2012) Sharing point-of-sale data: Challenges and opportunities. MDM special report, Modern Distribution Management, Niwot, CO.Google Scholar
• Kroger (2017) 2016 Kroger Fact Book (Kroger Company, Cincinnati).Google Scholar
• Kroger (2018) 2017 Kroger Fact Book (Kroger Company, Cincinnati).Google Scholar
• Ladley J (2012) Data Governance: How to Design, Deploy, and Sustain an Effective Data Governance Program (Morgan Kaufmann, Waltham, MA).Google Scholar
• Lee H, So K, Tang C (2000) The value of information sharing in a two-level supply chain. Management Sci. 46(5):626–642.Link, Google Scholar
• Lin J, Liu Q, Fournier-Viger P, Hong T, Voznak M, Zhan J (2016) A sanitization approach for hiding sensitive itemsets based on particle swarm optimization. Engrg. Appl. Artificial Intelligence 53(August):1–18.Crossref, Google Scholar
• Menon S, Sarkar S (2007) Minimizing information loss and preserving privacy. Management Sci. 53(1):101–116.Link, Google Scholar
• Menon S, Sarkar S (2016) Privacy and big data: Scalable approaches to sanitize large transactional databases for sharing. MIS Quart. 40(4):963–981.Crossref, Google Scholar
• Menon S, Sarkar S, Mukherjee S (2005) Maximizing accuracy of shared databases when concealing sensitive patterns. Inform. Systems Res. 16(3):256–270.Link, Google Scholar
• Munves G (2013) Wake up, retailers! Make money from your big data. Chain Store Age (April 3), https://chainstoreage.com/news/wake-retailers-make-money-your-big-data/.Google Scholar
• Navale G, Mali S (2019) Lossless and robust privacy preservation of association rules in data sanitization. Cluster Comput. 22(S1):1415–1428.Crossref, Google Scholar
• Nayar A (2019) Top 3 insights that suppliers gain from downstream data. Manthan (blog). https://www.manthan.com/blogs/vl-top-3-insights-that-suppliers-gain-from-downstream-data/.Google Scholar
• Nunez M, Garfinkel R, Gopal R (2007) Stochastic protection of confidential information in databases: A hybrid of data perturbation and query restriction. Oper. Res. 55(5):890–908.Link, Google Scholar
• Oliveira S, Zaïane O (2002) Privacy preserving frequent itemset mining. Clifton C, Estivill-Castro V, eds. Proc. IEEE ICDM Workshop Privacy, Security Data Mining (Australian Computer Society, Darlinghurst, NSW, Australia), 43–54.Google Scholar
• Olson J (2003) Data Quality: The Accuracy Dimension (Morgan Kaufmann, San Francisco).Google Scholar
• Padmanabhan B, Tuzhilin A (2006) On characterization and discovery of minimal unexpected patterns in rule discovery. IEEE Trans. Knowledge Data Engrg. 18(2):202–216.Crossref, Google Scholar
• Power D (2002) What is the “true story” about data mining, beer and diapers? DSS News 3(23). http://www.dssresources.com/newsletters/66.php.Google Scholar
• Reddy M, Wang R (1995) Estimating data accuracy in a federated database environment. Bhalla S, ed. Proc. Sixth Internat. Conf. Inform. Systems Management Data (Springer, Berlin), 115–134.Crossref, Google Scholar
• Redman T (2016) Bad data costs the U.S. $3 trillion per year. Harvard Bus. Rev. (September 22), https://hbr.org/2016/09/bad-data-costs-the-u-s-3-trillion-per-year.Google Scholar
• Redman R (2020) Kroger gives CPG advertisers more sales visibility. Supermarket News (February 14), https://www.supermarketnews.com/marketing/kroger-gives-cpg-advertisers-more-sales-visibility.Google Scholar
• Retail Touchpoints (2017) Winning with data sharing: Driving new analytical and revenue opportunities. White paper, Retail Touchpoints, Hasbrouck Heights, NJ.Google Scholar
• Retail Velocity (2019) Retailer POS data sources. Accessed October 3, 2021, https://www.retailvelocity.com/downstream-pos-data-retail-sales-analysis-sources.Google Scholar
• Sahar S (1999) Interestingness via what is not interesting. Proc. Fifth ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM, New York), 332–336.Google Scholar
• Sahinidis NV (2014) BARON 14.3.1: Global Optimization of Mixed-Integer Nonlinear Programs (User’s Manual).Google Scholar
• Silberschatz A, Tuzhilin A (1995) On subjective measures of interestingness in knowledge discovery. Fayyad UM, Uthurusamy R, eds. Proc. First Internat. Conf. Knowledge Discovery Data Mining (AAAI Press, Palo Alto, CA), 275–281.Google Scholar
• Stavropoulos E, Verykios V, Kagklis V (2016) A transversal hypergraph approach for the frequent itemset hiding problem. Knowledge Inform. Systems 47(3):625–645.Crossref, Google Scholar
• Talebi B, Dehkordi M (2018) Sensitive association rules hiding using electromagnetic field optimization algorithm. Expert Systems Appl. 114:155–172.Crossref, Google Scholar
• Telikani A, Shahbahrami A (2017) Optimizing association rule hiding using combination of border and heuristic approaches. Appl. Intelligence 47(2):544–557.Crossref, Google Scholar
• Telikani A, Shahbahrami A (2018) Data sanitization in association rule mining: An analytical review. Expert Systems Appl. 96:406–426.Crossref, Google Scholar
• Telikani A, Gandomi A, Shahbahrami A, Dehkordi M (2020) Privacy-preserving in association rule mining using an improved discrete binary artificial bee colony. Expert Systems Appl. 144:113097.Crossref, Google Scholar
• Terry L (2015) CGT/RIS retailer/supplier shared data study: A supplement to Consumer Goods Technology and RIS News. Report, RSi Retail Solutions, San Jose, CA.Google Scholar
• Verykios V, Elmagarmid A, Bertino E, Saygin Y, Dasseni E (2004) Association rule hiding. IEEE Trans. Knowledge Data Engrg. 16(4):434–447.Crossref, Google Scholar
• Wang R, Strong D (1996) Beyond accuracy: What data quality means to data consumers. J. Management Inform. Systems 12(4):5–33.Crossref, Google Scholar
• Weinbaum A (2017) 9 strategies that will encourage distributors to submit channel POS data. Report, Computer Market Research, San Diego.Google Scholar
• Weinswig D (2020) Measuring the value of retail data sharing and analytics. Coresight Research/SPS commerce research report, Coresight Research, New York.Google Scholar
• Wu Y, Chiang C, Chen A (2007) Hiding sensitive association rules with limited side effects. IEEE Trans. Knowledge Data Engrg. 19(1):29–41.Crossref, Google Scholar
• Zhang L, Wang W, Zhang Y (2019) Privacy preserving association rule mining: Taxonomy, techniques, and metrics. IEEE Access 7:45032–45047.Crossref, Google Scholar