Cloaked Classifiers: Pseudonymization Strategies on Sensitive Classification Tasks (2024)

Abstract: Protecting privacy is essential when sharing data, particularly in the case of an online radicalization dataset that may contain personal information. In this paper, we explore the balance between preserving data usefulness and ensuring robust privacy safeguards, since regulations like the European GDPR shape how personal information must be handled. We share our method for manually pseudonymizing a multilingual radicalization dataset, ensuring performance comparable to the original data. Furthermore, we highlight the importance of establishing comprehensive guidelines for processing sensitive NLP data by sharing our complete pseudonymization process, our guidelines, the challenges we encountered as well as the resulting dataset.

## introduction radicalization, fostered by online propaganda and offline indoctrination, has been the primary driver in most terror attacks and eruptions of public violence over the past decade @xcite @xcite . it can be defined as a process by which an individual or group adopts increasingly radical viewpoints in opposition to a political, social, or religious system @xcite . these viewpoints cover, for example, far-right ideologies, religiously inspired extremism, and extreme conspirationism. such content can spread rapidly, especially through social media, making radicalization challenging to detect @xcite . natural language processing (nlp) methods have been used to detect and analyze radicalization mechanisms such as propaganda, recruitment, networking, data manipulation, and disinformation @xcite @xcite . however, the effectiveness of such detection models depends on the availability and quality of training and evaluation datasets. protecting user privacy, especially for sensitive tasks, is imperative when sharing such datasets. finding the right balance between the obligation to build accurate anonymization methods and the need to maintain a decent level of performance is hard, as pertinent information may be contained through some identifiers (usernames, urls, locations, etc.) and their associated socio-demographic or geographic markers. hence, a brutal anonymization of a dataset can hinder its usability, especially in a domain where radicalization clues are often found through these indicators @xcite . ensuring the privacy of individuals is critical, especially in light of regulations such as the general data protection regulation (gdpr) 1 . this is why we believe that despite implementing various laws to minimize harm and protect sensitive information, there is a need to explore how technological advancements intersect with data protection laws and impact the collection, storage, and use of confidential data @xcite . in this work, we present our methodology for the manual pseudonymization of a radicalization dataset that (i) ensures performance to be comparable to the original data while maintaining its semantic properties and (ii) protects user privacy. we emphasize the importance of establishing a standard framework for privacy and usefulness when processing sensitive nlp data by sharing the complete pseudonymization process for our datasets and the challenges we faced @xcite . it is a highly sensitive task that requires 100% accuracy; any oversight can render the dataset invalid. our dataset includes english, french, and arabic content from various sources such as forums, telegram and other social media platforms. the con-tent covers different radicalization domains (from white supremacy to jihadism) for each language. our dataset will be available upon publication foot_1 . the manual annotation process we devised guarantees a high level of precision and enables us to better explore the interaction of our nlp tools and improve user safety. furthermore, a critical component of our methodology involves identifying the exceptions for which anonymization does not need to be applied. for example, keeping well-known events and public figures enables us to leverage the knowledge embedded in the language model about specific entities and prevent pseudonymization from corrupting the relationships and alignment between named entities and other elements within the text, thereby enhancing the effectiveness of our system. our evaluation results show that models trained on our pseudonymized data maintain similar levels of performance to their original counterparts. to summarize, our contributions are as follows: ## methodology we argue that the sensitive nature of certain tasks requires human annotators; therefore, a considerable amount of our pseudonymization process is done manually. our guidelines are based on three primary sources: legal texts and recommendations from the french cnil and the gdpr, existing research on data anonymization for nlp, and a thorough analysis of our corpus. as far as we know, no work has been published on the pseudonymization of radicalization data. we have also not found any official, standardized method for pseudonymizing textual data, neither from the gdpr/cnil nor the literature. ## experiments in this section, we analyze the variation of the performance of the model in different scenarios and compare the use of anonymized data to original data for radicalization detection task. ## challenges public figures and influencers the lines between public figures, "influencers", and "private figures" are often blurred, making it challenging to determine if a journalist for a small news website should be considered a public figure. similarly, categorizing scholars and less renowned authors also poses difficulties. links redirecting towards radicalized content and far-right media websites it was often tough to decide what was to be anonymized for two reasons: the definition of "mainstream" can become entirely subjective, especially when a medium can be considered renowned in its circle but not enough for global recognition. moreover, even when a medium is categorized as mainstream, leaving it as such still poses an ethical dilemma, as it can contribute to sharing propaganda. data related to terrorists and attackers in the english and mainly arabic datasets, there were a lot of names of deceased terrorists, mainly from the far-right or from isis. while it is common for isis terrorists to have acquired names that do not always correspond to their birth names, and thus the risk of identification is lower, it is still a dilemma as to what should be left in the dataset. ## conclusion in this paper, we presented our approach to pseudonymization specifically tailored for a radicalization dataset. our method aimed to fill the gap in research on pseudonymization in sensitive domains, such as online radicalization. our technique balances the need for privacy protection while maintaining the usefulness of the data for research and analysis. we highlighted the challenges encountered during the pseudonymization process, particularly the nuances of handling different types of personal data. these challenges underscore the importance of a detailed and cautious approach. our multilingual radicalization dataset will be released upon publication. we advocate for developing a standardized framework for pseudonymizing sensitive nlp data. overall, our work contributes to the growing body of research advocating for enhanced privacy measures in the processing and sharing of sensitive data, aligning with recent efforts to establish universal standards for categorizing and anonymizing personally identifiable information @xcite .