ADKAM: A-Diversity K-Anonymity Model via Microaggregation

Cheng, Liang; Cheng, Shiduan; Jiang, Fan

doi:10.1007/978-3-319-17533-1_36

Cited by 3 publications

(1 citation statement)

References 18 publications

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“…The main characteristic of our model is that it can simplify the calculation of the integrated information of the access history by using the number and the frequency of access. With the proposed model, we can protect privacy from the collision of different databases as well as the update of the database items, which cannot be effectively handled by many of the exciting methods such as k-anonymity [4][5] and differential privacy [6] [7].…”

Section: Introductionmentioning

confidence: 99%

Access Control for Privacy Protection for Dynamic and Correlated Databases

Zhu

Zhang

Feng

et al. 2015

2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity)

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The characteristics of volume, variety, velocity and value for big data have made present privacy protection methods less effective in the protection of user privacy to meet the emerging requirements. In this paper, we analyze the spatial and temporal effects the access to the data on privacy disclosure and propose an access control model to protect user privacy that is related to the number and the frequency of access in the access history of the requester. By introducing the notions of the privacy threshold, the requested items and the access history, the proposed access control model can make the decision on whether to allow the current access request to the protected privacy information. This method can cope with the dynamic and correlative nature of the data for privacy protection while reducing the cost of computation.

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Section: Introductionmentioning

confidence: 99%