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Homomorphic Encryption (HE) is a powerful cryptographic primitive to address privacy and security issues in outsourcing computation on sensitive data to an untrusted computation environment. Comparing to secure Multi-Party Computation (MPC), HE has advantages in supporting non-interactive operations and saving on communication costs. However, it has not come up with an optimal solution for modern learning frameworks, partially due to a lack of efficient matrix computation mechanisms. In this wor

We present the first homomorphically encrypted logistic regression outsourcing model based on the critical observation that the precision loss of classification models is sufficiently small so that the decision plan stays still.

We present a practical solution for outsourcing analysis tools such as logistic regression analysis while preserving the data confidentiality.

In order to enhance security of cyber-physical systems, it is important to protect the signals from sensors to the controller, and from the controller to the actuator, because the attackers often steal and compromise those signals. One immediate solution could be encrypting the signals, but in order to perform computation in the controller, they should be decrypted before computation and encrypted again after computation. For this, the controller keeps the secret key, which in turn increases vul

Controlling or accessing remotely has become a prevalent form of operating numerous types of platforms and infrastructure. An exploding number of vehicles such as drones or cars, in particular, are being controlled wirelessly or connected through networks. This has brought unanimous concern that today's networked vehicle systems are vulnerable to attacks and the results could be fatal. Unfortunately, in contrast to active investigation on the security of the vehicles themselves, sensors, or comm

Homomorphic encryption (HE) is one of promising cryptographic candidates resolving privacy issues in machine learning on sensitive data such as biomedical data and financial data. However, HE-based solutions commonly suffer from relatively high computational costs due to a large number of iterations in the optimization algorithms such as gradient descent (GD) for the learning phase. In this paper, we propose a new method called ensemble GD for logistic regression, a commonly used machine learnin

Machine learning applications are intensively utilized in various science fields, and increasingly the biomedical and healthcare sector. Applying predictive modeling to biomedical data introduces privacy and security concerns requiring additional protection to prevent accidental disclosure or leakage of sensitive patient information. Significant advancements in secure computing methods have emerged in recent years, however, many of which require substantial computational and/or communication ove

Fully Homomorphic Encryption (FHE) is a cryptographic primitive that allows performing arbitrary operations on encrypted data. Since the conception of the idea in [RAD78], it has been considered a holy grail of cryptography. After the first construction in 2009 [Gen09], it has evolved to become a practical primitive with strong security guarantees. Most modern constructions are based on well-known lattice problems such as Learning With Errors (LWE). Besides its academic appeal, in recent years F