Citations: A data distortion by probability distribution

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C. Liew, W. Choi, C. Liew "Data Distortion by Probability Distribution," ACM TODS, 10, 3, pp. 395--411, 1985.

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A Privacy-Enhanced Microaggregation Method - Li, Zhu, Wang, Jajodia (2002) (Correct)

....These methods include but are not limited to the following: Additive noise approach [7] Random noise is added to the protected variables and then, when necessary, a reidentification swapping methodology is employed to assure confidentiality. Data distortion by probability distribution [8]. The underlying probability distribution function of the original data set is identified, and then a data set is generated from the estimated distribution function to replace the original data set. Data swapping [11] The data records are ranked in ascending order and then each ranked value is ....

Liew, C.K., Choi, U.J., Liew, C.J.: A data distortion by probability distribution. ACM Trans. on Database Systems 10(3) (1985) 395-441

Hippocratic Databases - Agrawal, Kiernan, Srikant, Xu (2002) (12 citations) (Correct)

.... checking for possible compromises [8] suppression of data cells of small size [9] and clustering entities into mutually exclusive atomic populations [61] The perturbation family includes swapping values between records [12] replacing the original database by a sample from the same distribution [33] [42] adding noise to the values in the database [52] 57] adding noise to the results of a query [4] and sampling the result of a query [11] Hippocratic databases share with statistical databases the goal of preventing disclosure of private information, and hence some of the techniques ....

C. K. Liew, U. J. Choi, and C. J. Liew. A data distortion by probability distribution. ACM Transactions on Database Systems, 10(3):395--411, 1985.

On the Design and Quantification of Privacy Preserving Data.. - Agrawal, Aggarwal (2001) (45 citations) (Correct)

....sensitive information about individuals. Some interesting discourses on the nature of privacy in the context of recent trends in information technology may be found in [3, 5, 10, 11, 12, 13] This has lead to a considerable amount of focus on privacy preserving data collection and mining methods [1, 6, 7, 8, 15]. An innovative approach for privacy preserving data mining was recently proposed in [1] This technique relies on two facts: # Users are not equally protective of all values in their records. Thus, users may be willing to provide modi ed values of certain elds by the use of a (publically ....

C. K. Liew, U. J. Choi, and C. J. Liew. A data distortion by probability distribution. ACM TODS, 10(3):395-411, 1985.

Auditing Boolean Attributes - Kleinberg, Papadimitriou, Raghavan (2000) (5 citations) (Correct)

....of the salary information This is the classical statistical database security problem, studied extensively since the 1970 s; see [1] for a survey. The main approaches to this problem involve perturbing the data so as to maintain their statistical characteristics but prevent their compromise [13, 11, 16], to perturb the responses for the same purpose, 2, 8] to restrict the size or overlap of the statistical queries [10, 9] or, finally (and closer to our concerns here) to audit the # Department of Computer Science, Cornell University, Ithaca NY 14853. Email: kleinber cs.cornell.edu. Supported ....

C. Liew, W. Choi, C. Liew "Data Distortion by Probability Distribution," ACM TODS, 10, 3, pp. 395--411, 1985.

Privacy-Preserving Data Mining - Agrawal, Srikant (2000) (98 citations) (Correct)

.... of data cells of small size (e.g. Cox80] and clustering entities into mutually exclusive atomic populations (e.g. YC77] The perturbation family includes swapping values between records (e.g. Den82] replacing the original database by a sample from the same distribution (e.g. LST83] LCL85] Rei84] adding noise to the values in the database (e.g. TYW84] War65] adding noise to the results of a query (e.g. Bec80] and sampling the result of a query (e.g. Den80] There are negative results showing that the proposed techniques cannot satisfy the conflicting objectives of ....

Chong K. Liew, Uinam J. Choi, and Chung J. Liew. A data distortion by probability distribution. ACM TODS, 10(3):395--411, 1985.

Auditing Boolean Attributes - Jon Kleinberg Christos (Correct)

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C. Liew, W. Choi, C. Liew "Data Distortion by Probability Distribution," ACM TODS, 10, 3, pp. 395--411, 1985.

Privacy-Preserving Alert Correlation: A Concept Hierarchy.. - Dingbang Xu And (2005) (1 citation) (Correct)

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C. Liew, U. Choi, and C. Liew. A data distortion by probability distribution. ACM Transactions on Database Systems, 10(3):395--411, September 1985.

A Condensation Approach to Privacy Preserving Data Mining - Charu Aggarwal And (Correct)

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Liew C. K., Choi U. J., Liew C. J.: A data distortion by probability distribution. ACM TODS Journal, 10(3) (1985) 395-411.

Microdata Disclosure Risk in Database Privacy Protection - Crises (2004) (Correct)