T-Closeness Slicing: A New Privacy-Preserving Approach for Transactional Data Publishing
Published Online:11 Sep 2018https://doi.org/10.1287/ijoc.2017.0791
References
- (1993) Mining association rules between sets of items in large databases. Buneman P, Jajodia S, eds. Proc. ACM SIGMOD Internat. Conf. Management of Data (ACM Press, New York), 207–216.Google Scholar
- (2012) An efficient transaction data publication using slicing. Middle-East J. Sci. Res. 12(12):1808–1811.Google Scholar
- (2006) A face is exposed for AOL searcher no. 4417749. New York Times (August 9).Google Scholar
- (2005) Data privacy through optimal K-anonymization. Proc. 21st Internat. Conf. Data Engrg. (IEEE Computer Society, Washington, DC), 217–228.Google Scholar
- (2010) ρ-uncertainty: Inference proof transaction anonymization. Proc. VLDB Endowment (PVLDB) 3(1):1033–1044.Crossref, Google Scholar
- (1986) Disclosure-limited data dissemination. J. Amer. Statist. Assoc. 81(393):10–28.Crossref, Google Scholar
- (2010) Privacy-preserving data publishing: A survey of recent developments. ACM Comput. Surveys 42(4):1–53.Crossref, Google Scholar
- (2011) Anonymous publication of sensitive transactional data. IEEE Trans. Knowledge Data Engrg. 23(2):161–174.Crossref, Google Scholar
- (2012) Data Mining: Concepts and Techniques (Elsevier, Waltham, MA).Crossref, Google Scholar
- (2013) SLOMS: A privacy preserving data publishing method for multiple sensitive attributes microdata. J. Software 8(12):3096–3104.Crossref, Google Scholar
- (1990) Finding Groups in Data—An Introduction to Cluster Analysis (John Wiley & Sons, Hoboken, NJ).Google Scholar
- (1993) Measures of disclosure risk and harm. J. Official Statist. 9(2):313–331.Google Scholar
- (2005) Incognito: Efficient full-domain K-anonymity. Özcan F, ed. Proc. ACM SIGMOD Internat. Conf. Management of Data (ACM Press, New York),49–60.Google Scholar
- (2006) Mondrian multidimensional K-anonymity. Proc. 22nd Internat. Conf. Data Engrg. (IEEE Computer Society, Washington, DC), 25–35.Google Scholar
- (2007) t-closeness: Privacy beyond k-anonymity and l-diversity. Proc. IEEE 23rd Internat. Conf. Data Engrg. (IEEE Computer Society, Washington, DC), 106–115.Google Scholar
- (2010) Closeness: A new privacy measure for data publishing. IEEE Trans. Knowledge Data Engrg. 22(7):943–956.Crossref, Google Scholar
- (2012) Slicing: A new approach for privacy preserving data publishing. IEEE Trans. Knowledge Data Engrg. 24(3):561–574.Crossref, Google Scholar
- (2008) Adaptive data reduction for large-scale transaction data. Eur. J. Oper. Res. 188(3):910–924.Crossref, Google Scholar
- (2009) Against classification attacks: A decision tree pruning approach to privacy protection in data mining. Oper. Res. 57(6):1496–1509.Link, Google Scholar
- (2014) Digression and value concatenation to enable privacy-preserving regression. MIS Quart. 38(3):679–698.Crossref, Google Scholar
- (2010a) The disclosure of diagnosis codes can breach research participants’ privacy. J. Amer. Medical Informatics Assoc. 17(3):322–327.Crossref, Google Scholar
- (2010b) Anonymizing transaction data to eliminate sensitive inferences. Bringas PG, Hameurlain A, Quirchmayr G, eds. Database and Expert Systems Applications. DEXA 2010. Lecture Notes in Computer Science, Vol. 6261 (Springer, Berlin), 400–415.Crossref, Google Scholar
- (2007) L-diversity: Privacy beyond K-anonymity. ACM Trans. Knowledge Discovery Data 1(1):1–52.Crossref, Google Scholar
- (1967) Some methods for classification and analysis of multivariate observations. Cam LML, Neyman J, eds. Proc. 5th Berkeley Sympos. Math. Statist. Probab., Vol. 1 (University of California Press, Berkeley, CA), 281–297.Google Scholar
- (2005) Maximizing accuracy of shared databases when concealing sensitive patterns. Inform. Systems Res. 16(3):256–270.Link, Google Scholar
- (2008) Robust de-anonymization of large sparse data sets. Proc. IEEE Symp. Security Privacy (IEEE Computer Society, Washington, DC), 111–125.Google Scholar
- (2007) Hiding the presence of individuals from shared databases. Chan CY, Ooi BC, Zhou A, eds. Proc. ACM SIGMOD Internat. Conf. Management of Data (ACM Press, New York), 665–676.Google Scholar
- (2001) Protecting respondent’s privacy in microdata release. IEEE Trans. Knowledge Data Engrg. 13(6):1010–1027.Crossref, Google Scholar
- (2015) A survey on privacy preserving data mining. Proc. 2nd Internat. Conf. Electronics Commun. Systems (ICECS) (IEEE, Washington, DC), 1740–1744.Google Scholar
- (2002a) K-anonymity: A model for protecting privacy. Internat. J. Uncertainty Fuzziness Knowledge-Based Systems 10(5):557–570.Crossref, Google Scholar
- (2002b) Achieving K-anonymity privacy protection using generalization and suppression. Internat. J. Uncertainty Fuzziness Knowledge-Based Systems 10(5):571–588.Crossref, Google Scholar
- (2014) A review on privacy preserving data mining: Techniques and research challenges. Internat. J. Comput. Sci. Inform. Technol. 5(2):2310–2315.Google Scholar
- (2008) Privacy-preserving anonymization of set-valued data. Proc. Very Large Data Bases Conf., Auckland (IEEE Computer Society, Washington, DC),115–125.Google Scholar
- (2006) Privacy protection: P-sensitive K-anonymity property. Proc. 22nd Internat. Conf. Data Engrg. Workshops (IEEE Computer Society, Washington, DC), 94–103.Google Scholar
- (2008) A survey of association rule hiding methods for privacy. Aggarwal CC, Yu PS, eds. Privacy-Preserving Data Mining. Advances in Database Systems, Vol. 34 (Springer, New York), 267–289.Crossref, Google Scholar
- (2014) China’s Digital Transformation: The Internet’s Impact on Productivity and Growth (McKinsey Global Institute, Shanghai, China). Accessed August 26, 2014, http://mckinseychina.com/chinas-digital-transformation/.Google Scholar
- (2010) Privacy preservation in transaction databases based on anatomy technique. Proc. 5th Internat. Conf. Comput. Sci. Ed. (IEEE Computer Society, Washington, DC), 173–178.Google Scholar
- (2006a) Personalized privacy preservation. Chaudhuri S, Hristidis V, Polyzotis N, eds. Proc. ACM SIGMOD Internat. Conf. Management of Data (ACM Press, New York),229–240.Google Scholar
- (2006b) Anatomy: Simple and effective privacy preservation. Proc. 32nd Internat. Conf. Very Large Data Bases (VLDB) (ACM Press, New York), 139–150.Google Scholar
- (2008) Anonymizing transaction databases for publication. Proc. 32nd Internat. Conf. Very Large Data Bases (VLDB) (ACM Press, New York), 767–775.Google Scholar
- (2014) A data anonymous method based on overlapping slicing. Proc. IEEE 18th Internat. Conf. Comput. Supported Cooperative Work Design (IEEE Computer Society, Washington, DC), 124–128.Google Scholar
- (2001) Real world performance of association rule algorithms. Lee D, Schkolnick M, Provost FJ, Srikant R, eds. Proc. 7th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM Press, New York), 401–406.Google Scholar

