Supervisory Control and Data Acquisition (SCADA) systems are the industrial control systems and operational infrastructure that can monitor and control the electricity grid. Electricity grids are increasingly transforming from the one-directional way of generating, transmitting, and distributing electricity to smart grids that are multi-directional in the way they monitor, automate, and remotely operate the power sector. SCADA systems are increasingly under cyber attacks illustrating growing vulnerabilities to the smart grids. The U.S. power industry notes the importance of SCADA chain cyber risks and the need to take proactive measures (timely patching of vulnerabilities) to mitigate the risks. However, not all vulnerabilities are always exploited by attackers; and not all vulnerabilities can be patched in resource-constrained scenarios. Therefore, the patch sequence needs to be strategic and efficient.

The rapid proliferation of biometrics has led to growing concerns about the security and privacy of the biometric data (template). A biometric uniquely identifies an individual and unlike passwords, it cannot be revoked or replaced since it is unique and fixed for every individual. To address this problem, many biometric template protection methods using fully homomorphic encryption have been proposed. But, most of them (i) are computationally expensive and practically infeasible (ii) do not support operations over real valued biometric feature vectors without quantization (iii) do not support packing of real valued feature vectors into a ciphertext (iv) require multishot enrollment of users for improved matching performance. To address these limitations, we propose a secure and privacy preserving method for biometric template protection using fully homomorphic encryption. The proposed method is computationally efficient and practically feasible, supports operations over real valued feature vectors without quantization and supports packing of real valued feature vectors into a single ciphertext. In addition, the proposed method enrolls the users using one-shot enrollment. To evaluate the proposed method, we use three face datasets namely LFW, FEI and Georgia tech face dataset. The encrypted face template (for 128 dimensional feature vector) requires 32.8 KB of memory space and it takes 2.83 milliseconds to match a pair of encrypted templates. The proposed method improves the matching performance by ~ 3% when compared to state-of-the-art, while providing high template security.