Inverse document frequency-based sensitivity scoring for privacy analysis

Coban Ö., Inan A., ÖZEL S. A.

SIGNAL IMAGE AND VIDEO PROCESSING, cilt.16, sa.3, ss.735-743, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 16 Sayı: 3
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1007/s11760-021-02013-1
  • Dergi Adı: SIGNAL IMAGE AND VIDEO PROCESSING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC, zbMATH
  • Sayfa Sayıları: ss.735-743
  • Anahtar Kelimeler: Privacy risk analysis, Online social networks, Inverse-document frequency, RISK
  • Atatürk Üniversitesi Adresli: Hayır

Özet

Privacy risk analysis of online social network (OSN) users aims at generating a risk score for each OSN user such that higher scores potentially imply a greater risk of privacy violation. Privacy risk analysis is typically carried out over a response matrix (R) where any matrix element r(ij) indicates the portion of the OSN that the user i shares his/her attribute j. Most of the existing work relies on the mathematical framework of item response theory to derive sensitivity and visibility components from R. In this study, we propose interpreting R to be a term-document matrix and consequently suggest using the inverse document frequency (IDF) method as the sensitivity component. Experiments performed on both synthetic and real-world datasets show that the proposed IDF-based method can be used as a sensitivity component.