• DocumentCode
    3309406
  • Title

    Probabilistic measure on aggregations [data security]

  • Author

    Lin, T.Y.

  • Author_Institution
    Dept. of Math. & Comput. Sci., San Jose State Univ., CA, USA
  • fYear
    1990
  • fDate
    3-7 Dec 1990
  • Firstpage
    286
  • Lastpage
    294
  • Abstract
    Proposes a probabilistic type of measure theory which estimates the amount of security relevant information of every subset of a given aggregate. This probabilistic measure theory provides each application a means or mechanism to furnish the system a numerical measure to assist the system security officer for making decisions on releasing or downgrading the internal data of the aggregate. A scenario for solving the aggregation problems during the design phase is proposed. In developing this work, two guiding principles are applied: Minimum Aggregation Principle (MAP) and the Maximum Protection Principle (MPP). MAP keeps both the number of elements in an aggregation and the number of aggregates to a minimum, while MPP keeps the unnecessary risks to a minimum
  • Keywords
    probabilistic logic; security of data; Maximum Protection Principle; Minimum Aggregation Principle; aggregation problems; data security; probabilistic measure theory; system security; Aggregates; Computer science; Computer security; Data security; Estimation theory; Guidelines; Information security; Mathematics; Military computing; Query processing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Security Applications Conference, 1990., Proceedings of the Sixth Annual
  • Conference_Location
    Tucson, AZ
  • Print_ISBN
    0-8186-2105-2
  • Type

    conf

  • DOI
    10.1109/CSAC.1990.143787
  • Filename
    143787