: 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.
Akash Shah, M. A. Wani, Ravi Prakash Chaturvedi et al.· Computers, Materials & C...· 0 citations
We propose an extension to Zynerator, a Model-Driven Architecture framework for automated microservice generation, that embeds DevSecOps principles directly at the modeling stage through semantic decorators. These decorators enable the automated synthesis of secure back-end and front-end components together with operational artifacts, including authentication and authorization modules, audit trails, monitoring dashboards, and DevSecOps pipelines covering SAST, DAST, testing, and deployment. The approach addresses a key limitation of the original Zynerator framework, namely the absence of explicit DevSecOps integration, and supports a security-by-design methodology that reduces reliance on specialized DevSecOps expertise. Through a detailed e-commerce case study and empirical evaluation against manual development and existing Model-Driven Architecture tools, we show that the enhanced framework reduces development effort, strengthens security posture, and accelerates DevSecOps adoption. These findings indicate that DevSecOps-aware model-driven engineering offers a viable pathway toward secure, auto-mated software delivery.
Younes Zouani, Mohamed Lachgar, Youssef Harrati et al.· International Journal of Adv...· 0 citations