Human-Inspired Obfuscation for Model Unlearning: Local and Global Strategies with Hyperbolic Representations (2025)

Abstract: Large language models (LLMs) achieve remarkable performance across various domains, largely due to training on massive datasets. However, this also raises growing concerns over the exposure of sensitive and private information, making model unlearning increasingly critical.However, existing methods often struggle to balance effective forgetting with maintaining model utility. In this work, we propose HyperUnlearn, a human-inspired unlearning framework. We construct two types of fuzzy data—local and global—to simulate forgetting, and represent them in hyperbolic and Euclidean spaces, respectively. Unlearning is performed on a model with frozen early layers to isolate forgetting and preserve useful knowledge.Experiments demonstrate that HyperUnlearn effectively forgets sensitive content while maintaining the model’s language understanding, fluency, and benchmark performance, offering a practical trade-off between forgetting and capability preservation.

## introduction large language models (llms) @xcite @xcite @xcite have rapidly ascended to a position of prominence, demonstrating remarkable capabilities across a diverse array of natural language processing tasks and revolutionizing various application domains @xcite @xcite @xcite . one key factor behind their outstanding performance is that they are trained on massive datasets. @xcite @xcite . however, these massive datasets may contain privacy-sensitive information, leading to potential privacy breaches. ## preliminary our method involves a more fine-grained division of the dataset, which necessitates the use of additional variables to distinguish between different data types in the representation space. let m θ v denote the initial vanilla model with parameters θ, which has not undergone any pretraining or unlearning. the data used for training is divided into two disjoint sets: the retain set d r and the forget set d f , such that @xmath0, where d r ∩ d @xmath1. each data sample in d is represented as a question-answer pair (x, y), where x denotes the input query, and y is the corresponding label. the model m v is first trained on the combined dataset d, after which the unlearning procedure is applied with the goal of selectively forgetting the information contained in d f while retaining the knowledge from d r . our method utilizes both euclidean and hyperbolic spaces for representation learning. the standard euclidean space is denoted as e n , representing the conventional flat space used in most deep learning settings. to adjust semantically clustered information, we additionally embed data in hyperbolic space. among several hyperbolic models, we adopt the lorentz model l k n due to its numerical stability and expressive capability. the n-dimensional lorentz model with curvature -1/k is defined as: here, vector @xmath2, the first dimension is taken as the time-axis, denoted x 0 , and the remaining n dimensions as the spatial-coordinates, denoted @xmath3, •⟩ l denotes the lorentzian inner product, given by: for any point @xmath4, and a tangent vector @xmath5, the exponential map of u back onto the hyperbolic manifold is defined as: here, ∥u∥ @xmath6, u⟩ l denotes the lorentzian norm of u. we use exp k o to move vectors from euclidean space to hyperbolic space, considering euclidean vectors to be tangent vectors at the origin @xmath7, 0, . . . , 0) t of the hyperbolic space @xcite , and we use the logarithmic map log k o to perform the inverse computation. ## hyperunlearn our method aims to erase privacy-sensitive information from pretrained models with minimal impact on their general capabilities. to this end, we propose a three-stage framework combining fuzzu data generation, semantic representations, and freezing-aware unlearning. the overall pipeline is illustrated in figure 4 . subsequent sections delve deeper into each stage. ## conclusion in this paper, we propose hyperunlearn, a novel framework for effective and interpretable model unlearning. inspired by cognitive theories of forgetting, we simulate memory loss using two types of obfuscated datalocal and global fuzzinessto study their impact on unlearning behavior. to enhance control, we project unlearn data into hyperbolic space for concentrated semantics, while retaining euclidean space for general data. combined with a early-layer freezing strategy, this hybrid design improves precision and stability. extensive experiments show that hyperun-learn effectively removes sensitive information while preserving model utility, achieving balance between forgetting and performance retention.