Reducing Privacy Risks in Online Self-Disclosures with Language Models (2024)

Abstract: Self-disclosure, while being common and rewarding in social media interaction, also poses privacy risks. In this paper, we take the initiative to protect the user-side privacy associated with online self-disclosure through detection and abstraction. We develop a taxonomy of 19 self-disclosure categories and curate a large corpus consisting of 4.8K annotated disclosure spans. We then fine-tune a language model for detection, achieving over 65% partial span F _1 . We further conduct an HCI user study, with 82% of participants viewing the model positively, highlighting its real-world applicability. Motivated by the user feedback, we introduce the task of self-disclosure abstraction, which is rephrasing disclosures into less specific terms while preserving their utility, e.g., “Im 16F” to “I’m a teenage girl”. We explore various fine-tuning strategies, and our best model can generate diverse abstractions that moderately reduce privacy risks while maintaining high utility according to human evaluation. To help users in deciding which disclosures to abstract, we present a task of rating their importance for context understanding. Our fine-tuned model achieves 80% accuracy, on-par with GPT-3.5. Given safety and privacy considerations, we will only release our corpus and models to researcher who agree to the ethical guidelines outlined in Ethics Statement.

## introduction self-disclosure -the communication of personal information to others @xcite -is prevalent in online public discourse. disclosing personal information allows users to seek social support, build community, solicit context-specific advice, and explore aspects of their identity that they feel unsafe exploring offline @xcite . consider the following (hypothetical, but representative) reddit post: figure 1 : our model can provide diverse abstractions for self-disclosures of any length to suit user preferences. this approach effectively reduces privacy risks without losing the essence of the message. ## fine-grained self-disclosure detection to mitigate privacy leaks and alert people about their self-disclosures, it is essential to highlight specific text segments that disclose personal information, rather than simply classifying sentences as containing disclosures. to cover a wide spectrum of disclosures, we design a detailed taxonomy of 19 self-disclosure categories, which is more extensive than prior work (see related work in §6). we further construct an annotated corpus for training automatic models to detect self-disclosures at word-level, which supports our user study in §3. ## user study to understand how real users think about our disclosure detection model for protecting their privacy, we recruited 21 reddit users through prolific for an interview study-a step that differentiates our approach from prior disclosure identification work. this user study and its analysis were led by three authors with expertise in hci, privacy, and nlp. ## self-disclosure abstraction building upon insights from our user study, we introduce a novel task, self-disclosure abstraction, which rephrases disclosures with less specific details while preserving the content utility (see examples in figure 1 ). ## importance rating of self-disclosures to highlight and abstract self-disclosure more selectively, as users suggested in §3, we consider an additional task that rates the importance of each disclosure within context. task definition. given the disclosure span and its surrounding context, the task is to estimate how important this disclosure is for others to understand the user's message and communication goals. we consider three levels: low, moderate, and high, corresponding to disclosures that can be removed, essential but can be abstracted, and have to be kept as it is (see app. g for details). for disclosures that appear in a post's title or body, we consider both the title and body of the post as context. for disclosures in comments (i.e., replies to the main post), the context extends to the entire comment and its parent comment in the reply chain, if existing. training data. compared to abstraction, humans perform more effectively than llms on importance rating. we use the same train/dev/test split as the abstraction experiment and have each instance annotated by three in-house annotators. 24% instances reach a consensus and 65% have agreement between two annotators. only 11% exhibit complete disagreement. we also calculate krippendorff's α as 0.29. this fair level of agreement is anticipated given the task's subjective nature. for example, people have different opinions about whether details like age in a dating post should be retained ("32") or abstracted ("early 30s"). there often isn't a clear cut between low and moderate, moderate and high; yet, they provide useful signals to users. further discussions are in appendix e. for training labels, we take the majority vote or moderate if the annotators choose all three levels. model. we fine-tune llama-2-7b with various input-output formats as in the abstraction experiment ( §4). we use gpt-3.5 to generate the reasoning that leads to the human-assigned ratings. evaluation results. given the task subjectivity, we measure accuracy by considering a prediction as correct if it matches any one of the three annotations. figure 3 shows the accuracy of each method in comparison with gpt-3.5 and gpt-4. we find that fine-tuning on thought process degrades performance across all input format performance. the top performing fine-tuned model achieves 80.37%, on par with gpt-3.5's 80.98%. ## related work there is an excellent survey-position paper by @xcite , which has provided a comprehensive review of literature in the nlp for privacy area. it identified one key research challenge as: "most importantly, they (nlp approaches) are limited to predefined categories of entities and ignore how less conspicuous text elements may also play a role in re-identifying the individual. for instance, the family status or physical appearance of a person may lead to re-identification but will rarely be considered as categories to detect." -which motivated this very work of ours. we discuss some related works, including the newer ones, below. online self-disclosure detection. most existing research addressed self-disclosure detection in social media as sentence or document classification, which could not accurately pinpoint the specific disclosure spans. many prior work focused on one specific kind of self-disclosure, such as medical and mental health conditions @xcite @xcite @xcite @xcite , sexual harassment @xcite @xcite , personal opinions or sentiments @xcite , and employment history @xcite . other research considered all types of personal information that a user can reveal as a single category @xcite @xcite @xcite @xcite . a few works considered self-disclosures of multiple classes, but covered only a small number of categories @xcite , or relied on dictionary/rule-based methods @xcite , or limited to a particular context (e.g., bragging @xcite and news comments @xcite ). to address these limitations, we emphasize detecting self-disclosure at the span level and broadening the coverage to include 19 distinct categories. this allows for a more finegrained detection of privacy leaks for users. ## conclusion we push the first steps in protecting user-side privacy in online self-disclosures. our disclosure detection model trained on our new fine-grained corpus with span-level annotations achieves over 65% of partial span f 1 and is further validated through an hci user study, highlighting its real-world applicability. responding to the need from participants for balancing privacy risk reduction with message utility, we propose a novel task of self-disclosure abstraction, and explore various fine-tuning methods to generate three diverse abstractions. our human evaluation shows that the best model can provide diverse abstractions that reduce privacy risks while highly preserving utility. we further fine-tune a model to rate the importance of the selfdisclosure on understanding user's perspective and context. this model reaches 80% accuracy, matching the performance of gpt-3.5, thereby helping users to decide which disclosures to abstract. overall, we believe our work paves the way for a new direction of using llms to protect user-side privacy.