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The explosion in volume and variety of data offers enormous potential forresearch and commercial use. Increased availability of personal data is ofparticular interest in enabling highly customised services tuned to individualneeds. Preserving the privacy of individuals against reidentification attacksin this fast-moving ecosystem poses significant challenges for a one-size fitsall approach to anonymisation.  In this paper we present ($k$,$\epsilon$)-anonymisation, an approach thatcombines the $k$-anonymisation and $\epsilon$-differential privacy models intoa single coherent framework, providing privacy guarantees at least as strong asthose offered by the individual models. Linking risks of less than 5\% areobserved in experimental results, even with modest values of $k$ and$\epsilon$.  Our approach is shown to address well-known limitations of $k$-anonymity and$\epsilon$-differential privacy and is validated in an extensive experimentalcampaign using openly available datasets.

($k$,$\epsilon$)-Anonymity: $k$-Anonymity with $\epsilon$-Differential Privacy