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Quantitatively Measuring Privacy in Interactive Query Settings Within RDBMS Framework.

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Summary
This summary is machine-generated.

This paper introduces PriDe, a new quantitative privacy metric for database management systems. PriDe measures privacy risk during interactive queries, helping organizations monitor data access by third parties.

Keywords:
data analtyicselectronic privacyn-gramsprivacy scorerelational database management systems (RDBMS)

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Area of Science:

  • Computer Science
  • Information Security

Background:

  • Database Management Systems (DBMS) are widely used for data access, yet privacy risk measurement remains under-addressed.
  • Existing methods often fail to quantify privacy erosion during interactive query sessions.

Purpose of the Study:

  • To propose PriDe, a novel quantitative metric for measuring privacy risk in relational database management systems.
  • To provide a method for monitoring data access privacy in interactive query settings.

Main Methods:

  • Developed PriDe, a privacy score metric assessing privacy reduction based on previously retrieved attribute values.
  • Introduced a privacy equivalence relation to simplify privacy score computation.
  • The approach is applicable to BigSQL-style technologies.

Main Results:

  • PriDe quantifies privacy risk by measuring the reduction in privacy for a principal (user) over an interactive query session.
  • The proposed privacy equivalence relation facilitates efficient calculation of the privacy score.

Conclusions:

  • PriDe offers a practical solution for assessing and managing privacy risks in interactive database query environments.
  • This metric empowers organizations to monitor third-party data access and ensure privacy compliance.