Fully homomorphic encryption (FHE) is one of the prospective tools for privacy-preserving machine learning (PPML), and several PPML models have been proposed based on various FHE schemes and approaches. Although the FHE schemes are known as suitable tools to implement PPML models, previous PPML models on FHE such as CryptoNet, SEALion, and CryptoDL are limited to only simple and non-standard types of machine learning models. These non-standard machine learning models are not proven efficient and accurate with more practical and advanced datasets. Previous PPML schemes replace non-arithmetic activation functions with simple arithmetic functions instead of adopting approximation methods and do not use bootstrapping, which enables continuous homomorphic evaluations. Thus, they could not use standard activation functions and could not employ a large number of layers. In this work, we firstly implement the standard ResNet-20 model with the RNS-CKKS FHE with bootstrapping and verify the implemented model with the CIFAR-10 dataset and the plaintext model parameters. Instead of replacing the non-arithmetic functions with the simple arithmetic function, we use state-of-the-art approximation methods to evaluate these non-arithmetic functions, such as the ReLU and softmax, with sufficient precision. Further, for the first time, we use the bootstrapping technique of the RNS-CKKS scheme in the proposed model, which enables us to evaluate an arbitrary deep learning model on the encrypted data. We numerically verify that the proposed model with the CIFAR-10 dataset shows 98.43% identical results to the original ResNet-20 model with non-encrypted data. The classification accuracy of the proposed model is 92.43%±2.65%, which is quite close to that of the original ResNet-20 CNN model, 91.89%. It takes about 3 hours for inference on a dual Intel Xeon Platinum 8280 CPU (112 cores) with 172 GB memory. We think that it opens the possibility of applying the FHE to the advanced deep PPML model.

In recent years, many countries have been trying to integrate electronic health data managed by each hospital to offer more efficient healthcare services. Since health data contain sensitive information of patients, there have been much research that present privacy preserving mechanisms. However, existing studies either require a patient to perform various steps to secure the data or restrict the patient to exerting control over the data. In this paper, we propose patient-controlled attribute-based encryption, which enables a patient (a data owner) to control access to the health data and reduces the operational burden for the patient, simultaneously. With our method, the patient has powerful control capability of his/her own health data in that he/she has the final say on the access with time limitation. In addition, our scheme provides emergency medical services which allow the emergency staffs to access the health data without the patient's permission only in the case of emergencies. We prove that our scheme is secure under cryptographic assumptions and analyze its efficiency from the patient's perspective.

Fully homomorphic encryption (FHE) is one of the prospective tools for privacypreserving machine learning (PPML), and several PPML models have been proposed based on various FHE schemes and approaches. Although the FHE schemes are known as suitable tools to implement PPML models, previous PPML models on FHE encrypted data are limited to only simple and non-standard types of machine learning models. These non-standard machine learning models are not proven efficient and accurate with more practical and advanced datasets. Previous PPML schemes replace non-arithmetic activation functions with simple arithmetic functions instead of adopting approximation methods and do not use bootstrapping, which enables continuous homomorphic evaluations. Thus, they could not use standard activation functions and could not employ a large number of layers. The maximum classification accuracy of the existing PPML model with the FHE for the CIFAR-10 dataset was only 77% until now. In this work, we firstly implement the standard ResNet-20 model with the RNS-CKKS FHE with bootstrapping and verify the implemented model with the CIFAR-10 dataset and the plaintext model parameters. Instead of replacing the non-arithmetic functions with the simple arithmetic function, we use state-of-the-art approximation methods to evaluate these non-arithmetic functions, such as the ReLU, with sufficient precision [1]. Further, for the first time, we use the bootstrapping technique of the RNS-CKKS scheme in the proposed model, which enables us to evaluate a deep learning model on the encrypted data. We numerically verify that the proposed model with the CIFAR-10 dataset shows 98.67% identical results to the original ResNet-20 model with non-encrypted data. The classification accuracy of the proposed model is 90.67%, which is pretty close to that of the original ResNet-20 CNN model. It takes about 4 hours for inference on a dual Intel Xeon Platinum 8280 CPU (112 cores) with 512 GB memory. We think that it opens the possibility of applying the FHE to the advanced deep PPML model.

Order-revealing encryption is a useful cryptographic primitive that provides range queries on encrypted data since anyone can compare the order of plaintexts by running a public comparison algorithm. Most studies on order-revealing encryption focus only on comparing ciphertexts generated by a single client, and there is no study on comparing ciphertexts generated by multiple clients. In this paper, we propose the concept of multi-client order-revealing encryption that supports comparisons not only on ciphertexts generated by one client but also on ciphertexts generated by multiple clients. We also define a simulation-based security model for multi-client order-revealing encryption. The security model is defined with respect to the leakage function which quantifies how much information is leaked from the scheme. Next, we present two specific multi-client order-revealing encryption schemes with different leakage functions in bilinear maps and prove their security in the random oracle model. Finally, we give the implementation of the proposed schemes and suggest methods to improve the performance of ciphertext comparisons.

No abstract

No abstract

The studies about negotiation have been conducted in the various research fields. However, despite of its importance of employees’ concern, the number of studies for wage negotiation was few. Specifically, there have been no empirical studies directly examined the influence of participation of wage negotiation on the employees’ pay satisfaction and trust in Korea. The purpose of this study was to examine the influence of the opportunity and type of the wage negotiation on the pay satisfaction and trust in management. Data were collected from 315 workers employed in a variety of organizations in Korea and hierarchical multiple regression and MANCOVA were conducted to test hypothesis. The results of regression analysis indicated that the providing opportunity of wage negotiation have positive influence on the level of pay satisfaction and trust in management. Also, union negotiation rather than individual negotiation have positive influence on the level of pay satisfaction and trust in management. In addition, the results of MANCOVA was similar with that of regression analysis, however, there was no significant difference for trust in management and satisfaction of pay policy and management between union negotiation and individual negotiation.

No abstract