Fast Data Anonymization with Low Information Loss

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Data and Structural k-Anonymity in Social Networks (2009, methodology): “To measure the amount of information loss, several cost measures were introduced [4,7,13].”
Privacy-preserving anonymization of set-valued data (2008, methodology): “[21, 23, 5] present various methods to solve the (cid:96) -diversity problem eﬃciently.”
Differential privacy via wavelet transforms (2009, background): “Following previous work [9], [10], we assume that each nominal attribute Ai in T has an associated hierarchy, which is a tree where (i) each leaf is a value in the domain of Ai, and (ii) each internal node summarizes the leaves in its subtree.”
Differential Privacy via Wavelet Transforms (2009, background): “Following previous work [10], [11], we assume that each nominal attribute A i in T has an associated hierarchy, which is a tree where 1) each leaf is a value in the domain of A i , and 2) each internal node summarizes the leaves in its subtree.”
Effective Pattern Discovery for Text Mining (2012, background): “In this paper, Gabriel Ghinita, Panagiotis Karras, Panos Kalnis and Nikos Mamoulis (2007) [8] proposed an outline for privacy preservation.”