Probabilistic Unlearning-Based Defense for Robust and Efficient Edge Learning

Hyperdimensional computing (HDC), a brain-inspired paradigm, encodes information as high-dimensional vectors and forms associations through bundling. Yet, the ability to selectively forget information—critical for privacy, security, and adaptability—remains unexplored in HDC. We show that unlearning in HDC is difficult due to its memorization nature: vectors are directly stored in associative memory and persist, making it hard to forget specific data, while naive removal causes severe accuracy loss. We propose ClearAttack, a Computationally light and effective unlearning framework for HDC. ClearAttack tracks the influence of encoded vectors and offsets the impact of forgotten data with suitable substitutes, enabling in situ unlearning. To further mitigate accuracy loss, it incorporates selective retraining on sensitive samples. Experimental results show that ClearAttack unlearns data more effectively and efficiently than prior methods, achieving up to 3.6× higher forget score, accuracy within 0.2–0.6% of the oracle model, and up to 5.2× faster execution. Moreover, ClearAttack successfully eliminates the effect of poisoned data, demonstrating its utility for both accuracy and security. Because the complete unlearning procedure costs a fraction of a single retraining epoch and adds only 6.4% memory overhead, deletion requests and poisoning recovery can be handled directly on resource-constrained devices rather than requiring a full retraining pass.