Survey of Secure Two Parties Confidential Information Release in Vertical Partitioned Data
Languages of publication
To securely give person-exact fragile data from two data providers, whereby the mutual data maintains the necessary information for behind data mining tasks. Secured information distributed locations the problem of discovery delicate information when mining for useful data. In this paper, we address the problem of private data publishing, where personal uniqueness for the similar arrangement of people is detained by two parties. Differential privacy is a detailed security show that makes no doubt around an adversary's experience background knowledge. A differentially-private module ensures that the probability of any output (discharged information) is immediately as likely from all about the same information sets and therefore ensures that all outputs are merciless to any singular's data. As it were, a singular's privacy is not at risk in light of the interest in the data set. Specifically, we show a control for differentially private data discharge for vertically-distributed data between two parties in the semi-genuine adversary model. We first present a two-party convention for the exponential mechanism .This convention can be used as a sub convention by some other computation that requires the exponential component in a distributed setting. Likewise, we propose a two-party algorithm that discharges Differentially-private information in a secure way as per the significance of secure multiparty computation.
-  Barak B., Chaudhuri K., Dwork C., Kale, McSherry F. and Talwar K. (2007), ‘Privacy Accuracy, and Consistency Too: A Holistic Solution to Contingency Table Release’, Proc. ACM Symp. Principles of Database Systems (PODS ’07).
-  Bayardo R., and Agrawal R (2005), ‘Data privacy throughoptimal k-anonymization’. In Proceedings of the IEEE International Conference on Data Engineering (ICDE).
-  Chaudhuri K., Monteleoni C. and Sarwate A (2011), ‘Differentially private empirical risk minimization’. Journal of Machine Learning Research (JMLR), 12; 1069-1109.
-  Chaudhuri K., Sarwate A D. and Sinha K (2012), ‘Near-optimal differentially private principal components’, In Proceedings of the Conference on Neural Information Processing Systems.
-  Clifton C.,Kantarcioglu M., Vaidya J., Lin X., and Zhu M Y (2002), ‘Tools for privacy preserving distributed data mining’, ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD) Explorations Newsletter, 4(2); 28-34.
-  Dwork C., McSherry F., Nissim K., and Smith A. (2006), ‘Calibrating Noise to Sensitivity in Private Data Analysis’, Proc. Theory of Cryptography Conf. (TCC 06).
-  Dwork C., Kenthapadi K., McSherry F., Mironov I., and Naor M. (2006), ‘Our Data Ourselves: Privacy via Distributed Noise Generation’, Proc. 25th Ann. Int’l Conf. Theory and Applications of Cryptographic Techniques (EUROCRYPT ’06).
-  Dwork C. (2011), ‘A Firm Foundation for Private Data Analysis,’ Comm. ACM, Vol. 54, No. 1, pp. 86-95.
-  Fung B.C.M., Wang, K., Chen, R., Yu, P.S (2007), ‘Privacy-preserving data publishing’, A survey of recent developments. ACM Computing Surveys, Vol. 42, No. 4, pp. 1-53.
-  Fung B.C.M., Wang, K., Yu, P.S (2007), ‘Anonymizing classification data for privacy preservation’, IEEE Transaction on Knowledge and Data Engineering (TKDE), Vol. 19, No. 5, pp. 711-725.
-  Fung B C M., Wang K., Chen R. and Yu P. S. (2010), ‘Privacy-Preserving Data Publishing: A Survey of Recent Developments’, ACM Computing Surveys, Volume. 42, No. 4, pp. 1-53.
-  Jiang W and Clifton C (2006), ‘A Secure Distributed Framework for Achieving k-Anonymity,’ Very Large Data Bases J., Vol. 15, No. 4, pp. 316-333.
-  LeFevre, K., DeWitt, D.J., Ramakrishnan, R (2006). ‘Mondrian multidimensional k-anonymity’. In Proceedings of the IEEE International Conference on Data Engineering (ICDE).
-  Lindell Y and Pinkas B(2002), ‘Privacy Preserving Data Mining’, J. Cryptology, vol. 15, No. 3, pp. 177-206.
-  Machanavajjhala A., Kifer D., Gehrke J., Venkitasubramaniam, M. (2007), ‘ℓ-diversity: Privacy beyond k-anonymity’, ACM Transactions on Knowledge Discovery from Data (TKDD).
-  Mohammed N., Alhadidi D., Fung B C M (2014), ‘Secure Two-Party Differentially Private Data Release for Vertically Partitioned Data’, Proc. IEEE Transaction On Dependable And Secure Computing Vol. 11, No.1, pp. 59-70.
-  Mohammed N., Chen R., Fung B C M and Yu P S (2011), ‘Differentially Private Data Release for Data Mining’, Proc. ACM Int’l Conf. Knowledge Discovery and Data Mining (SIGKDD ’11).
-  Mohammed N., Fung B C M., and Debbabi M (2011), ‘Anonymity Meets Game Theory: Secure Data Integration with Malicious Participants’, Very Large Data Bases J., Vol. 20, No. 4, pp. 567-588.
-  Narayan A. and Haeberlen A. (2012), ‘DJoin: Differentially Private Join Queries over Distributed Databases’, Proc. 10th USENIX Conf. Operating Systems Design and Implementation (OSDI ’12).
-  Vaidya J and Clifton C (2002), ‘Privacy Preserving Association Rule Mining in Vertically Partitioned Data’, Proc. ACM Int’l Conf. Knowledge Discovery and Data Mining (SIGKDD ’02).
-  Vaidya J and Clifton C. (2003), ‘Privacy-Preserving k-Means Clustering over Vertically Partitioned Data,’ Proc. ACM Int’l Conf. Knowledge Discovery and Data Mining (SIGKDD ’03).
Publication order reference