… For the rest of this paper, we will call the original data set A and the anonymized data set
~A… 2.1 Disclosure Risk The threat to privacy from released anonymized data comes from what …

… utility and privacy of the spectral anonymization (SA) algorithm … the observed data grows
without bounds. Given an observed … loss when anonymizing data using another form of spectral …

… anonymized data. To make matters worse, there are several state-of-the-art graph data de-anonymization …
In this paper, we survey the graph data anonymization, de-anonymization, and …

… 20 data anonymization software tools. In addition, we also evaluated the practical feasibility
of performing anonymization on EHR data … guidance on practical EHR data anonymization. …

… data anonymization tool to support almost arbitrary combinations of a wide range of techniques
in a scalable manner. We then review the spectrum of … Next, we review the spectrum of …

… breaches are being reported [22], some resulting directly from a failure to anonymize or
de-identify the data properly [23]. In this context, it seems essential to review the literature …

… Finally, in Part III, we build upon the FTC's process-based approach to protecting data
subjects to identify how the full spectrum of techniques from the field of statistical disclosure …

… out using anonymous data. Because of the strong protection it brings, anonymized data
typically fall outside the scope of data protection laws, such as the EU’s General Data Protection …

… by the anonymization process, compared to using the raw data. We compare with two baseline
methods for coarse-grained time series data, namely Resampling and Singular Spectrum …

… defined in an ISO technical specification as a "particular type of anonymization that both
removes the association with a data subject and adds an association between a particular set of …