Genotype imputation estimates missing genotypes from the haplotype or genotype reference panel in individual genetic sequences, which boosts the potential of genome-wide association and is essential in genetic data analysis. However, the genetic sequences involve people's privacy, confirming an individual's identification and even disease information. This work proposes a secure genotype imputation model, which uses a linear regression model and the homomorphic encryption scheme over ciphertext to impute missing genotypes. The inference model is trained with float plaintext parameters, which are round into integers to avoid high complexity homomorphic evaluation on float number operations without bootstrapping operations. Even though the rounding parameters in the inference model are not the same as those in the trained model, We find that it will no effect on the outcome of the homomorphic prediction. Thus, a high-efficiency genotype imputation inference model over the ciphertext is obtained while keeping the high-security level. The simulation results indicate that the accuracy of the secure inference model is almost the same as the original model trained on float parameters. The secure inference model's accuracy is 98.6% for a single genotype.

An algorithm substitution attack (ASA) can undermine the security of cryptographic primitives by subverting the original implementation. An ASA succeeds when it extracts secrets without being detected. To launch an ASA on signature schemes, existing studies often needed to collect signatures with successive indices to extract the signing key. However, collection with successive indices requires uninterrupted surveillance of the communication channel and a low transmission loss rate in practice. This hinders the practical implementation of current ASAs, thus causing users to misbelieve that the threat incurred by ASA is only theoretical and far from reality. In this study, we first classify a group of schemes called extractable signatures that achieve traditional security (unforgeability) by reductions ending with key extraction, thus demonstrating that there is a generic and practical approach for ASA with this class of signatures. Further, we present the implementation of ASAs in which only two signatures and no further requirements are needed for the extraction of widely used discrete log-based signatures such as DSA, Schnorr, and modified ElGamal signature schemes. Our attack presents a realistic threat to current signature applications, which can also be implemented in open and unstable environments such as vehicular ad hoc networks. Finally, we prove that the proposed ASA is undetectable against polynomial time detectors and physical timing analysis.

Homomorphic encryption provides a way to perform deep learning over encrypted data and permits the user to encrypt the data before uploading, leaving the control of data on the user side. However, operations on encrypted data based on homomorphic encryption are time-consuming, especially in a deep convolutional neural network (CNN), which incorporates a large number of layers and operations. To speed up deep learning on encrypted data, we binarized the input data and weights of CNN model, while operations including the addition and multiplication in CNN become bit-wise operations. Therefore, the homomorphic evaluation of CNN can be performed in the binary field in a highly efficient way. We also construct an efficient pooling layer by designing circuits to perform comparison operations on the ciphertext. Simulation results clearly show that the convolution operation of the proposed model is at least 6.3 times faster than that of existing schemes. Last, our model exhibits no privacy leakage associated with the data being processed.