Data quality has become a pervasive challenge for organizations as they wrangle with large, heterogeneous datasets to extract value. Given the proliferation of sensitive and confidential information, it is crucial to consider data privacy concerns during the data cleaning process. For example, in medical database applications, varying levels of privacy are enforced across the attribute values. Attributes such as a patient’s country or city of residence may be less sensitive than the patient’s prescribed medication. Traditional data cleaning techniques assume the data is openly accessible, without considering the differing levels of information sensitivity. In this work, we take the first steps toward a data cleaning model that integrates privacy as part of the data cleaning process. We present a privacy-aware data cleaning framework that differentiates the information content among the attribute values during the data cleaning process to resolve data inconsistencies while minimizing the amount of information disclosed. Our data repair algorithm includes a set of data disclosure operations that considers the information content of the underlying attribute values, while maximizing data utility. Our evaluation using real datasets shows that our algorithm scales well, and achieves improved performance and comparable repair accuracy against existing data cleaning solutions.
