ERASE- EntRopy-based SAnitization of SEnsitive Data for Privacy Preservation

被引:0
|
作者
Medsger, Jeffrey [1 ]
Srinivasan, Avinash [1 ]
机构
[1] George Mason Univ, Dept Comp Sci, Fairfax, VA 22030 USA
关键词
Computer Drive Sanitization; Information Security; Digital Forensics;
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Effective and efficient sanitization of digital storage media is essential from both an information security as well as a digital forensics standpoint. The method proposed in this paper, ERASE, unlike brute force methods, computes the entropy of each data block in the target area, and if the entropy is within the specified sensitivity range, then that block is wiped with a user specified number of passes and pattern. ERASERS, an enhancement to ERASE that employs random sampling, is also proposed. ERASERS divides the given population into numerous subpopulations and uses random sampling to sample blocks from each subpopulation. Then, it computes the entropy of each sampled block, and if the entropy of any sampled block in the subpopulation is within the sensitive entropy range, which is a tunable parameter, then the entire subpopulation is wiped. The random sampling component of ERASERS gives organizations an alternative for a faster wipe as compared to regular brute force methods of overwriting. Our research resulted in different levels of sanitization for different time windows, a factor most organizations will consider when wiping large disks. According to Seagate, in 2011, the average size of hard drives they shipped that year was 590 GB [1]. Overwriting a 590 GB hard drive with one pass of random data, takes approximately 14.6 hours using dd. However, ERASE optimizes the data overwriting process allowing it to sanitize a 590GB hard drive in 9 : 5 hours, assuming 50% of the data on the drive is within the sensitive entropy range, thereby achieving a performance improvement of approximately 34.8%. If a wipe pattern of 1 pass using /dev/zero as the source is used, dd takes approximately 1.584 hours and ERASERS takes 0.85 hours in its best case performance and 1.580 hours in its worst case performance, assuming 50% of the data is within the sensitive entropy range. Thus, achieving a performance improvement in the range of 0.2% - 46.6%.
引用
收藏
页码:427 / 432
页数:6
相关论文
共 50 条
  • [31] Location Entropy-Based Privacy Protection Algorithm for Social Internet of Vehicles
    Xing, Ling
    Huang, Yuanhao
    Gao, Jianping
    Jia, Xiaofan
    Wu, Honghai
    Ma, Huahong
    WIRELESS PERSONAL COMMUNICATIONS, 2023, 130 (04) : 3009 - 3025
  • [32] Location Entropy-Based Privacy Protection Algorithm for Social Internet of Vehicles
    Ling Xing
    Yuanhao Huang
    Jianping Gao
    Xiaofan Jia
    Honghai Wu
    Huahong Ma
    Wireless Personal Communications, 2023, 130 : 3009 - 3025
  • [33] Entropy-Based Quantification of Privacy Attained Through User Profile Similarity
    Jagwani, Priti
    Kaushik, Saroj
    INTERNATIONAL JOURNAL OF INFORMATION SECURITY AND PRIVACY, 2021, 15 (03) : 19 - 32
  • [34] A Swarm-based Data Sanitization Algorithm in Privacy-Preserving Data Mining
    Ming-Tai, Jimmy
    Lin, Jerry Chun-Wei
    Djenouri, Youcef
    Fournier-Viger, Philippe
    Zhang, Yuyu
    2019 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2019, : 1461 - 1467
  • [35] Sensitive attribute privacy preservation of trajectory data publishing based on l-diversity
    Lin Yao
    Zhenyu Chen
    Haibo Hu
    Guowei Wu
    Bin Wu
    Distributed and Parallel Databases, 2021, 39 : 785 - 811
  • [36] Sensitive attribute privacy preservation of trajectory data publishing based on l-diversity
    Yao, Lin
    Chen, Zhenyu
    Hu, Haibo
    Wu, Guowei
    Wu, Bin
    DISTRIBUTED AND PARALLEL DATABASES, 2021, 39 (03) : 785 - 811
  • [37] Development and validation of a sensitive entropy-based measure for the water maze
    Maei, Hamid R.
    Zaslavsky, Kirill
    Wang, Afra H.
    Yiu, Adelaide P.
    Teixeira, Catia M.
    Josselyn, Sheena A.
    Frankland, Paul W.
    FRONTIERS IN INTEGRATIVE NEUROSCIENCE, 2009, 3
  • [38] An Entropy-Based Data Summarization Algorithm in Data Stream System
    Ouyang Lin
    Guo Qing-ping
    PACIIA: 2008 PACIFIC-ASIA WORKSHOP ON COMPUTATIONAL INTELLIGENCE AND INDUSTRIAL APPLICATION, VOLS 1-3, PROCEEDINGS, 2008, : 1823 - +
  • [39] Unified entropy-based sorting for reversible data hiding
    Jiajia Xu
    Weiming Zhang
    Ruiqi Jiang
    Nenghai Yu
    Multimedia Tools and Applications, 2017, 76 : 3829 - 3850
  • [40] Seismic Data Interpolation by Shannon Entropy-Based Shaping
    Huang, Weilin
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2022, 60