## 2 Related Work
### Privacy-Preserving Techniques in Image and Text Processing  
The escalating concerns surrounding data privacy in the digital age have spurred significant research and development in privacy-preserving techniques across various data modalities, including images and text. As AI systems increasingly process and analyze visual and textual information, the need to anonymize or redact sensitive data has become a critical imperative. This need is driven by both ethical considerations and increasingly stringent regulatory mandates. A diverse array of techniques has emerged to address this need, each offering different trade-offs between privacy protection, data utility, and computational overhead. For image data, common privacy-preserving methods typically involve obscuring or removing regions of interest that are likely to contain sensitive information. Blurring techniques, for example, apply Gaussian filters or similar smoothing operations to redact faces, license plates, or other identifiable features [16], effectively reducing the visual discernibility of these regions. Masking approaches, conversely, involve overlaying solid blocks or opaque shapes to completely conceal sensitive areas within an image. Pixelization, another technique, reduces the resolution of specific image regions, rendering details unrecognizable while preserving the overall context. For text, distinct anonymization techniques exist. Tokenization can replace sensitive words (names, addresses) with generic placeholders. Pseudonymization uses artificial identifiers, allowing analysis while obscuring direct links to individuals. Redaction, the most direct method, involves removing or replacing sensitive text segments, often with black bars or placeholders [11]. Our work primarily employs this redaction (masking) approach. We selected masking for this initial study because of its direct applicability to removing specific PII strings identified by the LLM within unstructured image data, aligning with a strict interpretation of removing defined PII categories under PDPA. However, the field of privacy preservation is rapidly evolving, offering alternative paradigms. K-anonymization, for example, is a prominent technique primarily used for structured data. Its core principle is to prevent re-identification by ensuring any individual's record is indistinguishable from at least k-1 other records within the dataset. This is typically achieved through generalization (e.g., replacing specific ages with broader age ranges, or full addresses with just the city) or suppression (removing certain identifying values altogether) [17]. Recent advancements focus on optimizing these processes, especially in the context of machine learning challenges, using techniques like iterative local search [18]. Crucially, k-anonymization contrasts with our masking approach: instead of completely removing information, it aims to strike a balance between data privacy and utility by retaining partial, generalized information. This reduces identification risk while potentially preserving more analytical value. While applying k-anonymization directly to unstructured label images presents unique challenges, it represents an important alternative privacy-utility trade-off focused on indistinguishability. Other advanced techniques like differential privacy add calibrated noise to data or query results to protect individuals while enabling aggregate analysis [19]. In the specific context of labels, blanket anonymization that obscures entire labels renders them useless. Therefore, intelligent and selective techniques are essential. Our approach focuses on targeted redaction of identified PII to preserve non-sensitive information utility. While AI techniques like differential privacy and federated learning are increasingly explored for protecting data during model training [20], our research leverages AI (specifically LLMs) for the distinct task of applying redaction to the data itself.