Integrating Private Databases for Data Analysis

Wang, Ke; Fung, Benjamin C. M.; Dong, Guozhu

doi:10.1007/11427995_14

Cited by 31 publications

(20 citation statements)

References 8 publications

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“…The solution is a simple and effective method as it uses cost effective algorithms for achieving anonymity. The solution proposed in [14] is based on tree data structure called TIPS. We base our solution on subset generation and selecting the most relevant subset.…”

Section: Resultsmentioning

confidence: 99%

A Dynamic Programming Approach for Privacy Preserving Collaborative Data Publishing

Reddy¹,

Raju²,

Kumari³

2011

IJCA

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Organizations share their data about customers for exploring potential business avenues. The sharing of data has posed several threats leading to individual identification. Owing to this, privacy preserving data publication has become an important research problem. The main goals of this problem are to preserve privacy of individuals while revealing useful information. An organization may implement and follow its privacy policy. But when two companies share information about a common set of individuals, and if their privacy policies differ, it is likely that there is privacy breach unless there is a common policy. One such solution was proposed for such a scenario, based on k-anonymity and cut-tree method for 2-party data. This paper suggests a simple solution for integrating nparty data using dynamic programming on subsets. The solution is based on thresholds for privacy and informativeness based on k-anonymity.

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Section: Resultsmentioning

confidence: 99%

A Dynamic Programming Approach for Privacy Preserving Collaborative Data Publishing

Reddy¹,

Raju²,

Kumari³

2011

IJCA

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“…There are some distributed anonymization methods in vertically partitioned data [10,14,6]. Mohammed et al [10,14] used the top-down approach and some secure computation protocols [9,15] in order to join the tables in multiple providers.…”

Section: Related Workmentioning

confidence: 99%

“…Mohammed et al [10,14] used the top-down approach and some secure computation protocols [9,15] in order to join the tables in multiple providers. The top-down approach is an algorithm to specialize a value qid of a quasiidentifier in the tables step by step.…”

Section: Related Workmentioning

confidence: 99%

Distributed Data Federation without Disclosure of User Existence

Takenouchi

Kawamura

Ohsuga

2012

Data and Applications Security and Privacy XXVI

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Abstract. Service providers collect user's personal information relevant to their businesses. Personal information stored by different service providers is expected to be combined to make new services. However, specific user records risk being identified from the combined personal information, and the user's sensitive information may be revealed. Also, personal information collected by a service provider must not be disclosed to other service providers because of security issues. Thus, several researchers have been investigating distributed anonymization protocols, which combine the personal information stored by the providers and sanitize it to ensure an anonymity policy with minimum disclosure. However, when providers have different sets of the users, there is a problem that the existence of users in either service provider may be revealed. This paper introduces a new notion, δ-max-site-presence, which indicates the probability of the existence of users being revealed in a distributed environment and a new distributed anonymization protocol for hiding the existence of users. Our evaluation results show that the proposed protocol can anonymize users in accordance with the policy of hiding their existence and user anonymity without too much information loss.

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“…[22] proposed a multi-dimensional generalization method, called InfoGain Mondrian, to identify a k-anonymous solution. [29][43] [44] addressed the extended data publishing scenarios, such as multiple releases and multiple data holders. [10] presented a suppression method for anonymizing high-dimensional sequential data.…”

Section: Related Workmentioning

confidence: 99%

Privacy-preserving data publishing for cluster analysis

Fung

Wang

et al. 2009

Data & Knowledge Engineering

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Releasing person-specific data could potentially reveal sensitive information about individuals. k-anonymization is a promising privacy protection mechanism in data publishing. Although substantial research has been conducted on k-anonymization and its extensions in recent years, only a few prior works have considered releasing data for some specific purpose of data analysis. This paper presents a practical data publishing framework for generating a masked version of data that preserves both individual privacy and information usefulness for cluster analysis. Experiments on real-life data suggest that by focusing on preserving cluster structure in the masking process, the cluster quality is significantly better than the cluster quality of the masked data without such focus. The major challenge of masking data for cluster analysis is the lack of class labels that could be used to guide the masking process. Our approach converts the problem into the counterpart problem for classification analysis, wherein class labels encode the cluster structure in the data, and presents a framework to evaluate the cluster quality on the masked data.

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Integrating Private Databases for Data Analysis

Cited by 31 publications

References 8 publications

A Dynamic Programming Approach for Privacy Preserving Collaborative Data Publishing

A Dynamic Programming Approach for Privacy Preserving Collaborative Data Publishing

Distributed Data Federation without Disclosure of User Existence

Privacy-preserving data publishing for cluster analysis

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