a method for obfuscating Web data

Rahat Masood, Dinusha Vatsalan, Muhammad Ikram, Mohamed Ali Kaafar

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

8 Citations (Scopus)


Users leave a trail of their personal data, interests, and intents while surfing or sharing information on the Web. Web data could therefore reveal some private/sensitive information about users based on inference analysis. The possible identification of information corresponding to a single individual by an inference attack holds true even if the user identifiers are encoded or removed in the Web data. Several works have been done on improving privacy of Web data through obfuscation methods. However, these methods are neither comprehensive, generic to be applicable to any Web data, nor effective against adversarial attacks. To this end, we propose a privacy-aware obfuscation method for Web data addressing these identified drawbacks of existing methods. We use probabilistic methods to predict privacy risk of Web data that incorporates all key privacy aspects, which are uniqueness, uniformity, and linkability of Web data. The Web data with high predicted risk are then obfuscated by our method to minimize the privacy risk using semantically similar data. Our method is resistant against adversary who has knowledge about the datasets and model learned risk probabilities using differential privacy-based noise addition. Experimental study conducted on two real Web datasets validates the significance and efficacy of our method. Our results indicate that the average privacy risk reaches to 100% with a minimum of 10 sensitive Web entries, while at most 0% privacy risk could be attained with our obfuscation method at the cost of average utility loss of 64.3%.
Original languageEnglish
Title of host publicationThe Web Conference 2018 - Proceedings of the World Wide Web Conference, WWW 2018
Place of PublicationSwitzerland
PublisherAssociation for Computing Machinery (ACM)
Number of pages10
ISBN (Electronic)9781450356398
Publication statusPublished - 2018
Event27th International World Wide Web Conference, WWW 2018 - Lyon, France
Duration: 23 Apr 201827 Apr 2018

Publication series

NameThe Web Conference 2018 - Proceedings of the World Wide Web Conference, WWW 2018


Conference27th International World Wide Web Conference, WWW 2018


  • Web Data Privacy
  • Privacy Risk Evaluation
  • Data Obfuscation
  • Adversarial Machine Learning
  • Probabilistic Model
  • Semantic Similarity
  • Data obfuscation
  • Privacy risk evaluation
  • Adversarial machine learning
  • Probabilistic model
  • Semantic similarity
  • Web data privacy

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