@inproceedings{,
    author = {Buff, Bianca; Kersting, Joschka; Geierhos, Michaela},
    title = {Detection of Privacy Disclosure in the Medical Domain : A Survey},
    editor = {},
    booktitle = { Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods},
    series = {},
    journal = {},
    address = {},
    publisher = {SCITEPRESS},
    edition = {},
    year = {2020},
    isbn = {978-989-758-397-1},
    volume = {},
    number =  {},
    pages = {630-637},
    url = {},
    doi = {10.5220/0009347506300637},
    keywords = {Identity Disclosure ; Privacy Protection ; Physician Review Website ; De-Anonymization ; Medical Domain},
    abstract = {When it comes to increased digitization in the health care domain, privacy is a relevant topic nowadays. This relates to patient data, electronic health records or physician reviews published online, for instance. There exist different approaches to the protection of individuals’ privacy, which focus on the anonymization and masking of personal information subsequent to their mining. In the medical domain in particular, measures to protect the privacy of patients are of high importance due to the amount of sensitive data that is involved (e.g. age, gender, illnesses, medication). While privacy breaches in structured data can be detected more easily, disclosure in written texts is more difficult to find automatically due to the unstructured nature of natural language. Therefore, we take a detailed look at existing research on areas related to privacy protection. Likewise, we review approaches to the automatic detection of privacy disclosure in different types of medical data. We provide a survey of several studies concerned with privacy breaches in the medical domain with a focus on Physician Review Websites (PRWs). Finally, we briefly develop implications and directions for further research.},
    note = {},
    
    
    
}