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M-score: estimating the potential damage of data leakage incident by assigning misuseability weight

Published: 08 October 2010 Publication History

Abstract

Over the past few years data leakage and data misuse have become a major concern for organizations. A data leakage or data misuse incident can damage an organization's reputation and brand name as well as compromise the privacy of its customers. Much research has been conducted in order to find a solution to these threats. Most methods are based on anomaly detection that tracks the user's behavior by examining the syntax of SQL queries in order to detect outlier queries. Other methods examine the data retrieved by the query. In this paper, we propose a new concept for analyzing the retrieved data - the Misuseability Weight. This approach focuses on assigning a score that represents the sensitivity level of the data exposed to the user. This measure predicts the ability of a user to exploit the exposed data in a malicious way. We suggest a new measure, the M-score, which assigns a misuseability weight to a table of data, propose some properties of the new measure and demonstrate its usefulness using over several leakage scenarios.

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  • (2015)A Dynamic Approach to Detect Anomalous Queries on Relational DatabasesProceedings of the 5th ACM Conference on Data and Application Security and Privacy10.1145/2699026.2699120(245-252)Online publication date: 2-Mar-2015
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      cover image ACM Conferences
      Insider Threats '10: Proceedings of the 2010 ACM workshop on Insider threats
      October 2010
      70 pages
      ISBN:9781450300926
      DOI:10.1145/1866886
      • General Chair:
      • Ehab Al-Shaer,
      • Program Chairs:
      • Brent Lagesse,
      • Craig Shue
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Published: 08 October 2010

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      Author Tags

      1. data leakage
      2. data misuse
      3. misuseability weight
      4. security measures

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      Insider Threats '10 Paper Acceptance Rate 7 of 14 submissions, 50%;
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      Cited By

      View all
      • (2021)Automated big text security classification2016 IEEE Conference on Intelligence and Security Informatics (ISI)10.1109/ISI.2016.7745451(103-108)Online publication date: 11-Mar-2021
      • (2015)A Dynamic Approach to Detect Anomalous Queries on Relational DatabasesProceedings of the 5th ACM Conference on Data and Application Security and Privacy10.1145/2699026.2699120(245-252)Online publication date: 2-Mar-2015
      • (2013)Self-protecting and self-optimizing database systemsProceedings of the 2013 ACM Cloud and Autonomic Computing Conference10.1145/2494621.2494631(1-10)Online publication date: 9-Aug-2013
      • (2012)An Autonomic Framework for Integrating Security and Quality of Service Support in DatabasesProceedings of the 2012 IEEE Sixth International Conference on Software Security and Reliability10.1109/SERE.2012.15(51-60)Online publication date: 20-Jun-2012
      • (2011)Eliciting domain expert misuseability conceptionsProceedings of the sixth international conference on Knowledge capture10.1145/1999676.1999721(193-194)Online publication date: 26-Jun-2011
      • (2011)Dynamic Sensitivity-Based Access ControlProceedings of 2011 IEEE International Conference on Intelligence and Security Informatics10.1109/ISI.2011.5984080(201-203)Online publication date: Jul-2011

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