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Information Theoretic and Statistical Drive Sanitization Models

Author

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  • Jeffrey Medsger
  • Avinash Srinivasan
  • Jie Wu

Abstract

Current drive sanitization techniques employ little or no intelligence to determine if the area being sanitized, with data overwriting, actually contains sensitive resident data. All data blocks in the target area are sanitized, utilizing brute-force sanitization techniques of one to several wipe passes. In reality, a significant number of drives needing sanitization may contain areas with no sensitive data—or even any data. Consequently, sanitizing such areas is counterintuitive and counterproductive. This article proposes two information-theoretic techniques—ERASE and ERASERS, which utilize an entropy measurement of data blocks for quick and effective drive sanitization. The first technique, ERASE, computes the entropy of each data block in the target area. Subsequently, all data blocks, which have an entropy within the user-specified sensitivity range, are wiped. The second technique, ERASERS, which is an extension of ERASE, employs random sampling to enhance the speed performance of ERASE. To achieve this goal, ERASERS divides the target area into subpopulations, performs random sampling of blocks from each subpopulation, and computes the entropy of each sampled block. If the entropy of any sampled block, within a subpopulation, is within the user-specified sensitive entropy range, the entire subpopulation is wiped.

Suggested Citation

  • Jeffrey Medsger & Avinash Srinivasan & Jie Wu, 2015. "Information Theoretic and Statistical Drive Sanitization Models," Journal of Information Privacy and Security, Taylor & Francis Journals, vol. 11(2), pages 97-117, April.
    • Handle: RePEc:taf:uipsxx:v:11:y:2015:i:2:p:97-117
    DOI: 10.1080/15536548.2015.1045380
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