Data Utility in Differential Privacy via Microaggregation-based k-Anonymity”

نویسنده

  • Jordi Soria-Comas
چکیده

In addition to the general-purpose SSE-based utility evaluation conducted and discussed in the body of the article, in this appendix we provide evaluation results for a specific data use, namely counting queries. The reason of focusing on this data use is that many related works on differentially-private data publishing aim at preserving the utility for counting queries over protected data [12–14,1,4,7,2]. We want to investigate how well our general-purpose method does for counting queries compared to methods that have been designed with this type of queries in mind.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Improving the Utility of Differential Privacy via Univariate Microaggregation

Differential privacy is a privacy model for anonymization that offers more robust privacy guarantees than previous models, such as k-anonymity and its extensions. However, it is often disregarded that the utility of differentially private outputs is quite limited, either because of the amount of noise that needs to be added to obtain them or because utility is only preserved for a restricted ty...

متن کامل

Utility-Preserving Differentially Private Data Releases Via Individual Ranking Microaggregation

Being able to release and exploit open data gathered in information systems is crucial for researchers, enterprises and the overall society. Yet, these data must be anonymized before release to protect the privacy of the subjects to whom the records relate. Differential privacy is a privacy model for anonymization that offers more robust privacy guarantees than previous models, such as k-anonym...

متن کامل

Improved Univariate Microaggregation for Integer Values

Privacy issues during data publishing is an increasing concern of involved entities. The problem is addressed in the field of statistical disclosure control with the aim of producing protected datasets that are also useful for interested end users such as government agencies and research communities. The problem of producing useful protected datasets is addressed in multiple computational priva...

متن کامل

Working Paper ENGLISH ONLY UNITED NATIONS ECONOMIC COMMISSION FOR EUROPE (UNECE) CONFERENCE OF EUROPEAN STATISTICIANS EUROPEAN COMMISSION STATISTICAL OFFICE OF THE EUROPEAN

The usual approach to generate k-anonymous data sets, based on generalization of the quasi-identifier attributes, does not provide any control on the variability of the confidential attributes within the k-anonymous groups. If the latter variability is too small, privacy is not sufficiently protected, while, for large variabilities, data utility is substantially damaged. Some refinements to the...

متن کامل

Mining Frequent Patterns Through Microaggregation in Differential Privacy

Frequent pattern mining has been widely employed to analyze transaction datasets, but the question of how sensitive information contained in a dataset should be protected remains remains relatively unanswered. The differential privacy model provides a robust privacy guarantee, but the k-anonymity model provides better dataset utility. In this paper, a synergetic approach is proposed to simultan...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2014