Gang Xiang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Gang Xiang has a total of 8 co-authors. Here are the top co-authors by number of shared publications:
- Berlin Wu
National Chengchi University
47 shared publications - Hung Tan Nguyen
New Mexico State University
47 shared publications - Vladik Kreinovich
Department of Computer Science, University of Texas at El Paso, 500 W. University, El Paso, TX 79968, USA
47 shared publications E. Mayorga
University of Texas at El Paso, 500 W. University, El Paso, 79968, USA
1 shared publicationL. Ginzburg
Applied Biomathematics, 100 North Country Rd., Setauket, NY 11733, USA
1 shared publicationL. Longpre
University of Texas at El Paso, 500 W. University, El Paso, 79968, USA
1 shared publication
Latest Publications
- 1 June 2013
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 611-616
Data anonymization that leads to the most accurate estimates of statistical characteristics: Fuzzy-motivated approach
To preserve privacy, the original data points (with exact values) are replaced by boxes containing each (inaccessible) data point. This privacy-motivated uncertainty leads to uncertainty in the statistical characteristics computed based on this data. In a previous paper, we described how to minimize this uncertainty under the assumption that we use the same standard statistical estimates for the desired characteristics. In this paper, we show that we can further decrease the resulting uncertainty if we allow fuzzy-motivated weighted estimates, and we explain how to optimally select the corresponding weights.
- 1 June 2013
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 611-616
Data anonymization that leads to the most accurate estimates of statistical characteristics: Fuzzy-motivated approach
To preserve privacy, the original data points (with exact values) are replaced by boxes containing each (inaccessible) data point. This privacy-motivated uncertainty leads to uncertainty in the statistical characteristics computed based on this data. In a previous paper, we described how to minimize this uncertainty under the assumption that we use the same standard statistical estimates for the desired characteristics. In this paper, we show that we can further decrease the resulting uncertainty if we allow fuzzy-motivated weighted estimates, and we explain how to optimally select the corresponding weights.
- 1 January 2012
- Published by Springer Nature
- p. 47-50
Computing under Interval Uncertainty: Computational Complexity
- 1 January 2012
- Published by Springer Nature
- p. 47-50
Computing under Interval Uncertainty: Computational Complexity
- 1 January 2012
- Published by Springer Nature
- p. 29-33
Computing under Interval Uncertainty: When Measurement Errors Are Small
- 1 January 2012
- Published by Springer Nature
- p. 29-33
Computing under Interval Uncertainty: When Measurement Errors Are Small
- 1 January 2012
- Published by Springer Nature
- p. 25-27
Computing under Interval Uncertainty: Traditional Approach Based on Uniform Distributions
- 1 January 2012
- Published by Springer Nature
- p. 25-27
Computing under Interval Uncertainty: Traditional Approach Based on Uniform Distributions
- 1 January 2012
- Published by Springer Nature
- p. 391-393
Beyond Traditional Fuzzy Uncertainty: Interval-Valued Fuzzy Techniques
- 1 January 2012
- Published by Springer Nature
- p. 391-393
Beyond Traditional Fuzzy Uncertainty: Interval-Valued Fuzzy Techniques
- 1 January 2012
- Published by Springer Nature
- p. 349-355
Beyond Interval Uncertainty: Case of Discontinuous Processes (Phase Transitions)
- 1 January 2012
- Published by Springer Nature
- p. 349-355
Beyond Interval Uncertainty: Case of Discontinuous Processes (Phase Transitions)
- 1 January 2012
- Published by Springer Nature
- p. 19-24
Computing under Fuzzy Uncertainty Can Be Reduced to Computing under Interval Uncertainty
- 1 January 2012
- Published by Springer Nature
- p. 19-24
Computing under Fuzzy Uncertainty Can Be Reduced to Computing under Interval Uncertainty
- 1 January 2012
- Published by Springer Nature
- p. 299-304
Applications to Computer Engineering: Timing Analysis of Computer Chips
- 1 January 2012
- Published by Springer Nature
- p. 299-304
Applications to Computer Engineering: Timing Analysis of Computer Chips
- 1 January 2012
- Published by Springer Nature
- p. 283-287
Applications to Information Management: How to Measure Loss of Privacy
- 1 January 2012
- Published by Springer Nature
- p. 283-287
Applications to Information Management: How to Measure Loss of Privacy
- 1 January 2012
- Published by Springer Nature
- p. 277-281
Applications to Information Management: How to Estimate Degree of Trust
- 1 January 2012
- Published by Springer Nature
- p. 277-281
Applications to Information Management: How to Estimate Degree of Trust