You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
The explosion in volume and variety of data offers enormous potential for research and commercial use. Increased availability of personal data is of particular interest in enabling highly customised services tuned to individual needs. Preserving the privacy of individuals against reidentification attacks in this fast-moving ecosystem poses significant challenges for a one-size fits all approach to anonymisation. In this paper we present ( @math , @math )-anonymisation, an approach that combines the @math -anonymisation and @math -differential privacy models into a single coherent framework, providing privacy guarantees at least as strong as those offered by the individual models. Linking risks of less than 5 are observed in experimental results, even with modest values of @math and @math . Our approach is shown to address well-known limitations of @math -anonymity and @math -differential privacy and is validated in an extensive experimental campaign using openly available datasets.

Cited Abstract(s):
[1]: This paper aims at answering the following two questions in privacy-preserving data analysis and publishing. The first is: What formal privacy guarantee (if any) does k-anonymization methods provide? k-Anonymization methods have been studied extensively in the database community, but have been known to lack strong privacy guarantees. The second question is: How can we benefit from the adversary's uncertainty about the data? More specifically, can we come up a meaningful relaxation of differential privacy [2, 3] by exploiting the adversary's uncertainty about the dataset? We now discuss these two motivations in more detail.
[1] the authors propose a way to achieve differential privacy and @math -anonymity on the same data release. In order to add a stochastic element to @math -anonymity (a deterministic system), the authors propose taking a sample at random from the original dataset and then processing this sample to achieve @math -anonymity prior to publication. The uncertainty introduced by randomly selecting users to be present in the released (but anonymised) dataset ensures differential privacy can also be achieved.