Abstract:
Distortion of data prior to publishing is one of the primary approaches to make sensitive data free of any illegal access or malicious use. Privacy customization has not ...Show MoreMetadata
Abstract:
Distortion of data prior to publishing is one of the primary approaches to make sensitive data free of any illegal access or malicious use. Privacy customization has not been emphasized and well-studied in related literature. In this paper, data owners' preferences and data attributes' characteristics are taken into consideration. A privacy customization strategy is proposed and accomplished via a group distortion technique based on matrix decomposition. Several privacy and utility measures are studied. The performance of the proposed strategy is evaluated and compared to a conventional full distortion method. Our evaluation demonstrates that the proposed strategy has some attractive properties including an improved utility. In this way, a tradeoff between privacy and utility becomes more feasible.
Date of Conference: 12-14 October 2012
Date Added to IEEE Xplore: 20 December 2012
ISBN Information:
ISSN Information:
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Privacy Protection ,
- Sensitive Data ,
- Data Owner ,
- Data Distortion ,
- Privacy Measures ,
- Error Rate ,
- Cross-entropy ,
- Key Properties ,
- Mutual Information ,
- Singular Value Decomposition ,
- Single Dataset ,
- Feature Selection Methods ,
- Non-negative Matrix Factorization ,
- Mining Algorithms ,
- Sensitive Attributes ,
- Privacy Breaches ,
- Privacy Requirements ,
- Distortion Levels ,
- Loss Of Privacy ,
- Conditional Mutual Information ,
- Future Research Plans ,
- Adult Dataset ,
- High Distortion
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Privacy Protection ,
- Sensitive Data ,
- Data Owner ,
- Data Distortion ,
- Privacy Measures ,
- Error Rate ,
- Cross-entropy ,
- Key Properties ,
- Mutual Information ,
- Singular Value Decomposition ,
- Single Dataset ,
- Feature Selection Methods ,
- Non-negative Matrix Factorization ,
- Mining Algorithms ,
- Sensitive Attributes ,
- Privacy Breaches ,
- Privacy Requirements ,
- Distortion Levels ,
- Loss Of Privacy ,
- Conditional Mutual Information ,
- Future Research Plans ,
- Adult Dataset ,
- High Distortion
- Author Keywords