Empowering users of social networks to assess their privacy risks

Vladimir Estivill-Castro, Peter Hough, Md Zahidul Islam

Research output: Book chapter/Published conference paperConference paperpeer-review

10 Citations (Scopus)


Millions of users place data about themselves on on-line social networks and, while probably they have an interest on some of this information to be publicly available, they certainly may consider some of this information shall remain confidential. Simultaneously, the data provides benefits as such data enables personalization which increases the quality of service; and thus, it is regularly analyzed with data mining techniques. Since privacy directly correlates to the control users have regarding the data about themselves, this paper provides a technique by which operators of on-line social networks can improve the service to their users by empowering the users to appraise the privacy risks that some information they provide results in others inferring confidential attributes
Original languageEnglish
Title of host publicationProceedings, 2014 IEEE International Conference on Big Data
EditorsW Chang, J Huan, N Cercone, S Pyne, V Honavar, J Lin, XT Hu, C Aggarwal, B Mobasher, J Pei, R Nambiar
Place of PublicationPiscataway, N.J
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)9781479956654
Publication statusPublished - 2014
Event2014 IEEE International Conference on Big Data (Big Data) - Hyatt Regency, Washington DC, United States
Duration: 27 Oct 201430 Oct 2014


Conference2014 IEEE International Conference on Big Data (Big Data)
Country/TerritoryUnited States
CityWashington DC
OtherIn recent years, “Big Data” has become a new ubiquitous term. Big Data is transforming science, engineering, medicine, healthcare, finance, business, and ultimately society itself. The IEEE International Conference on Big Data 2014 (IEEE BigData 2014) provides a leading forum for disseminating the latest research in Big Data Research, Development, and Applications. We solicit high-quality original research papers (including significant work-in-progress) in any aspect of Big Data with emphasis on 5Vs (Volume, Velocity, Variety, Value and Veracity):: big data science and foundations, big data infrastructure, big data management, big data searching and mining, big data privacy/security, and big data applications.


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