A blockchain as a trustworthy and secure decentralized and distributed network has been emerged for many applications such as in banking, finance, insurance, healthcare and business. Recently, many communities in blockchain networks want to deploy machine learning models to get meaningful knowledge from geographically distributed large-scale data owned by each participant. To run a learning model without data centralization, distributed machine learning (DML) for blockchain networks has been studied. While several works have been proposed, privacy and security have not been sufficiently addressed, and as we show later, there are vulnerabilities in the architecture and limitations in terms of efficiency. In this paper, we propose a privacy-preserving DML model for a permissioned blockchain to resolve the privacy, security, and performance issues in a systematic way. We develop a differentially private stochastic gradient descent method and an error-based aggregation rule as core primitives. Our model can treat any type of differentially private learning algorithm where non-deterministic functions should be defined. The proposed error-based aggregation rule is effective to prevent attacks by an adversarial node that tries to deteriorate the accuracy of DML models. Our experiment results show that our proposed model provides stronger resilience against adversarial attacks than other aggregation rules under a differentially private scenario. Finally, we show that our proposed model has high usability because it has low computational complexity and low transaction latency.

Existing sustainable IT services have several problems related to user authentication such as the inefficiency of managing the system security, low security, and low usability. In this paper, we propose a Fast IDentity Online (FIDO) authenticator that performs continuous authentication with implicit authentication based on user context and multimodal authentication. The proposed FIDO authenticator, a context-aware multimodal FIDO authentication (CAMFA) method, combines information such as the user context, state of the mobile device, and user biometrics, then applies implicit and explicit authentication methods to meet the level of authentication required by the service provider. This reduces the user’s explicit authentication burden and continually authenticates users at risk during the session. Moreover, it is able to respond to attacks such as the theft of the authentication method or session hijacking. To study the effectiveness of CAMFA, we ran a user study by collecting data from 22 participants over 42 days of activity on a practical Android platform. The result of the user study demonstrates that the number of explicit authentication requests could be reduced by half. Based on the results of this study, an advanced user authentication that provides multimodal and continuous authentication could be applied to sustainable IT services.

With the popularization of smart devices, companies are adopting bring-your-own-device or mobile office policies that utilize personal smart devices for work. However, as work data are stored on individual smart devices, critical security threats are emerging, such as the leakage of confidential documents. Enterprises want to address this issue by adapting enterprise mobility management (EMM) solutions. Appwrapping is among the core technologies in EMM solutions, enabling security function insertion or misused code patching without the original application (app) source code. Studies on permission control, misused code patching, security function insertion based on static policies, etc., have been conducted, but there are limitations such as poor user convenience and overhead. In this paper, we propose an AppWrapper toolkit to support dynamic polices. Basically it can insert security function execution code into apps by using appwrapping technology without the original source code. This code uses Java reflection to invoke security functions dynamically based on preset policies. Accordingly, after the initial appwrapping, the policy can be changed easily. In addition, even when multiple security functions are required, Java reflection can invoke multiple security functions dynamically and simultaneously without conflicting with the existing code. The AppWrapper toolkit also provides a log function to check in real time where the security function is needed. Hence, the policy-setting administrator can check the log in real time and implement the security function where needed. Our experimental results show that this technique improves significantly the efficiency, effectiveness, and convenience of adding security function execution code.

Short message service (SMS) is the most widely adopted multi-factor authentication method for consumer-facing accounts. However, SMS authentication is susceptible to vulnerabilities such as man-in-the-middle attack, smishing, and device theft. This study proposes implicit authentication based on behavioral pattern of users when they check an SMS verification code and environmental information of user proximity to detect device theft. User behavioral pattern is collected by using the accelerometer and gyroscope of a smart device such as a smartphone and smart watch. User environmental information is collected using device fingerprint, wireless access point, Bluetooth, and global positioning system information. To evaluate the performance of the proposed scheme, we perform experiments using a total of 1320 behavioral and environmental data collected from 22 participants. The scheme achieves an average equal error rate of 6.27% when using both behavioral and environmental data collected from only a smartphone. Moreover, it achieves an average equal error rate of 0% when using both behavioral and environmental data collected from a smartphone and smart watch. Therefore, the proposed scheme can be employed for more secure SMS authentication.