TY  - EJOU
AU  - Shah, Akash 
AU  - Wani, Mudasir Ahmad 
AU  - Chaturvedi, Ravi Prakash 
AU  - Kant, Shri 
AU  - Sindhwani, Nidhi 
AU  - Shakil, Kashish Ara 
AU  - Alshuhri, Sulieman 

TI  - HEbdMIA: Lightweight Logit Encryption for Membership Inference Defense
T2  - Computers, Materials \& Continua

PY  - 
VL  - 
IS  - 
SN  - 1546-2226

AB  - Membership Inference Attacks (MIAs) pose a significant privacy risk in machine learning by enabling adversaries to infer whether specific data samples were used during training, particularly in sensitive domains such as social media and mental health analytics. To address this challenge, this paper proposes HEbdMIA, a lightweight homomorphic encryption-based defense that operates at the post-inference stage by encrypting model output logits without requiring retraining or architectural modifications. The proposed approach preserves the relative ordering of predictions while obscuring confidence patterns exploited by MIAs. Experimental evaluation on DepInferAttack and BotInferAttack demonstrates that HEbdMIA achieves a reduction in MIA success rates of 31.0% and 27.3%, respectively, with an associated accuracy decrease of 29.3% and 26.4%, reflecting a controlled privacy and utility trade off. Additional analysis using precision, recall, F1-score, and ROC-AUC confirms a substantial decline in adversarial inference capability. These findings indicate that HEbdMIA provides an effective, scalable, and deployment-friendly solution for enhancing privacy in real-world machine learning systems.
KW  - Membership inference attack; homomorphic encryption; machine learning; encrypted data; logit encryption; model security

DO  - 10.32604/cmc.2026.082713