Poster: The impact of data sampling in the anonymization pipeline Jenno Verdonck, Kevin De Boeck, Michiel Willocx, Jorn Lapon, Vincent Naessens imec-DistriNet KU Leuven Ghent, Belgium firsname.lastname@kuleuven.be Abstract—An increasing number of companies are selling data as an additional source of revenue, or acquire data from other parties to optimize their business. In many cases, the shared data contains sensitive personal records. According to the GDPR regulation, personal data should be anonymized before it is released to third parties. A frequently applied technique is the k-anonymity metric, which ensures that every record in the dataset becomes indistinguishable from K other records through data generalization. This work combines generalization techniques with sampling. By adding a sampling step in the anonymization pipeline, additional uncertainty is introduced towards a potential attacker. As attackers can no longer be sure that an in- dividual is in the sampled dataset, the re-identification risk is mitigated. This work proposes and evaluates multiple sam- pling techniques. Both the privacy and the utility properties of the anonymized datasets are embraced. The utility of the anonymized datasets is further evaluated in a machine learning use-case. Index Terms—anonymization, sampling, privacy, utility 1. Introduction Data collection and processing have become aspects of increasing importance in the daily operation of many businesses and organizations. Amongst others, data is employed for optimizing production processes and to in- crease the effectiveness of marketing campaigns. Hence, sharing (or trading) data can be very lucrative, and might even bootstrap cooperation between organizations. While data sharing exposes great opportunities for companies, caution should be taken. First, the European GDPR regu- lation states that datasets containing personal information should be anonymized before they are released. This means that a record in the shared dataset can no longer be linked to an individual afterwards. Second, thoughtless release can undermine the competitiveness of companies. Anonymization techniques can be applied to mitigate these threats. An often cited and applied strategy for data anonymization relies on privacy metrics such as k- anonymity [1]. The goal of this metric is to generalize attribute values in such a way that each individual be- comes indistinguishable from at least k −1 other indi- viduals in the dataset. However, constructing a feasible anonymized dataset is no sinecure for most organizations. Some information is inevitably lost during anonymization caused by generalization. Hence, companies often struggle to find a satisfactory balance between the privacy and the utility of an anonymized dataset. Moreover, solely applying k-anonymity does not always protect against attackers with membership knowledge, a key assumption in the prosecutor attacker model [2]. This work argues that the aforementioned risk is mitigated by applying an additional sampling step in the anonymization pipeline. An attacker can no longer be sure that the target is in the dataset, making it harder to re-identify individuals. The prosecutor becomes a journalist, corresponding to the also well-known but less powerful journalist attacker model. Contributions. This paper presents preliminary results of our research on combining traditional anonymization tech- niques (i.e. k-anonymity) and sampling. Three different sampling strategies are presented, implemented and as- sessed. The impact of each strategy on both the utility and the privacy properties of the remaining data is evaluated in a practical machine-learning use-case. The remainder of this work is structured as follows. Section 2 points to related work. Section 3 describes different sampling strategies. Thereafter, Section 4 details the evaluation methodology. Lastly, Section 5 outlines the conclusions and points to future work. 2. Related work Sampling has been t