Research Output

Privacy Parameter Variation using RAPPOR on a Malware Dataset

  Stricter data protection regulations and the poor application of privacy protection techniques have resulted in a requirement for data-driven companies to adopt new methods of analysing sensitive user data. The RAPPOR (Randomized Aggregatable Privacy-Preserving Ordinal Response) method adds parameterised noise, which must be carefully selected to maintain adequate privacy without losing analytical value. This paper applies RAPPOR privacy parameter variations against a public dataset containing a list of running Android applications data. The dataset is filtered and sampled into small (10,000); medium (100,000); and large (1,200,000) sample sizes while applying RAPPOR with = 10; 1.0; and 0.1 (respectively low; medium; high privacy guarantees). Also, in order to observe detailed variations within high to medium privacy guarantees (= 0.5 to 1.0), a second experiment is conducted by progressively adjusting the value of over the same populations. The first experiment verifies the original RAPPOR studies using = 1 with a non-existent recoverability in the small sample size, and detectable signal from medium to large sample sizes as also demonstrated in the original RAPPOR paper. Further results, using high privacy guarantees, show that the large sample size, in contrast to medium, suffers 2.75 times more in terms of recoverability when increasing privacy guarantees from = 1.0 to 0.8. Overall, the paper demonstrates that high privacy guarantees to restrict the analysis only to the most dominating strings.

  • Date:

    06 September 2018

  • Publication Status:

    Published

  • DOI:

    10.1109/TrustCom/BigDataSE.2018.00133

  • Library of Congress:

    QA75 Electronic computers. Computer science

  • Dewey Decimal Classification:

    005.8 Data security

  • Funders:

    Edinburgh Napier Funded

Citation

Aaby, P., Mata De Acuña, J. J., Macfarlane, R., & Buchanan, W. J. (2018). Privacy Parameter Variation using RAPPOR on a Malware Dataset. In Proceedings of 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications (IEEE TrustCom-18), 8. doi:10.1109/TrustCom/BigDataSE.2018.00133

Authors

Keywords

Privacy parameter variation; privacy preservation; big data; RAPPOR;

Monthly Views:

Available Documents

  • pdf

    Privacy Parameter Variation using RAPPOR on a Malware Dataset

    216KB
    Number of Downloads in the past year: 5

    © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

  • Downloadable citations

    HTML BIB RTF