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Exposing safe correlations in transactional datasets

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Abstract

A particularly challenging problem for data anonymization is dealing with transactional data. Most anonymization methods assume homogeneous, independent and identically distributed (i.i.d.) data; “flattening” transactional data to satisfy this model results in wide, sparse data that does not anonymize well with traditional techniques. While there have been some approaches for generalization-based anonymization, bucketization techniques (e.g., anatomy) pose new challenges. In particular, bucketization provides the opportunity to learn correlations between data items, but also a risk of identifying individuals because of dependencies inferred from such correlations. We present a method that balances these issues, retaining the ability to discover correlations in the data, while hiding dependencies that would enable correlations to be used to link specific values to individuals. We introduce a correlation anonymization constraint that ensures correlations do not allow data to be linked to a specific individual, and an elastic safe grouping algorithm that meets this constraint while preserving data correlations. We evaluate the utility loss on a transactional rental dataset.

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Notes

  1. We assume User ID and Vin Number are independent and identically distributed (i.i.d.).

  2. Some data is anonymized/suppressed in order to meet the constraint; this is in keeping with privacy models that uses partial suppression by replacing individual’s values with a * to preserve privacy as in [34, 42] or encryption as in the model in [36] where some data is left encrypted, and only “safe” data is revealed.

  3. https://github.com/ElieChicha/ESC/blob/main/sourcedata.xlsx.

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Acknowledgements

The authors would like to acknowledge the National Council for Scientific Research of Lebanon (CNRS-L) and Univ. Pau & Pays Adour, UPPA-E2S, LIUPPA, for granting a doctoral fellowship to Elie Chicha.

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Authors and Affiliations

  1. LIUPPA, Universite Pau and Pays Adour, E2S UPPA, Anglet, France

    Elie Chicha & Richard Chbeir

  2. TICKET Lab., Antonine University, Hadat-Baabda, Lebanon

    Elie Chicha & Bechara Al Bouna

  3. Technische Universität Dresden, Dresden, Germany

    Kay Wünsche

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  1. Elie Chicha
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  2. Bechara Al Bouna
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  3. Kay Wünsche
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  4. Richard Chbeir
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Correspondence to Elie Chicha.

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Chicha, E., Al Bouna, B., Wünsche, K. et al. Exposing safe correlations in transactional datasets. SOCA 15, 289–307 (2021). https://doi.org/10.1007/s11761-021-00325-1

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