To address the growing privacy concerns in cloud computing, homomorphic encryption (HE) provides a secure computation framework that enables functions to be evaluated directly on encrypted data without revealing the underlying plaintext.However, the practical deployment of HE remains limited due to its high computational overhead and the lack of native support for HE-unfriendly operations, such as division, conditional branching, and logarithmic functions.To mitigate these limitations, recent studies have proposed computation paradigms based on precomputed lookup tables (LUTs), which allow HE-unfriendly functions to be evaluated indirectly by querying encrypted precomputed tables.This paper proposes a non-interactive model based on word-wise homomorphic encryption for evaluating arbitrary one-input functions over encrypted LUTs.Existing LUT-based approaches built on BFV or BGV schemes require precomputing outputs for all possible inputs, leading to substantial computational overhead, and are restricted to integer-valued computations.To overcome these limitations, we introduce a new LUT-based evaluation method by leveraging the Cheon-Kim-Kim-Song (CKKS) scheme, which supports approximate real-number arithmetic.Furthermore, our method is extended to support multiple real-number inputs, making it the first LUTbased HE approach capable of handling multi-input real-number functions.In addition, we design a structured rearrangement strategy during the LUT construction phase, which effectively reduces the overall computational cost during function evaluation.Experimental results demonstrate that the proposed method enables flexible and efficient real-number function evaluation while significantly reducing latency compared to existing approaches.In particular, for two-input function evaluation, our method achieves speedups ranging from approximately 1.14× to 3.40× over prior methods and for three-input function evaluation, achieves speedups ranging from approximately 1.09× to 1.16×.

To address the growing security challenges in cloud computing, homomorphic encryption (HE) offers a promising solution for a third party to evaluate functions and maintain data privacy even during calculation, as it allows for the evaluation on ciphertexts. However, the practical use of HE is constrained by limited arithmetic capabilities and high computational overhead. A promising strategy to mitigate these challenges is to evaluate HE-unfriendly functions through precomputed lookup tables (LUTs). This paper proposes a non-interactive model for evaluating arbitrary one-input functions using homomorphic encrypted LUTs with word-wise HE. Existing methods based on BFV/BGV schemes require precomputing outputs for all possible inputs, which leads to significant computational overhead. Furthermore, they are restricted to integer computations. This paper proposes a new LUT-based method that extends these approaches by employing the CKKS scheme, which supports approximate realnumber computation, thereby enhancing flexibility and efficiency. Our CKKS-based LUT computation method enables a flexible trade-off between accuracy and latency while supporting realnumber operations. Specifically, increasing the LUT size improves the output accuracy for a given function, allowing users to balance computational precision and processing speed according to their needs. Our experimental evaluation confirms that the proposed method achieves up to a $19 \%$ reduction in latency and up to an $\mathbf{8 1. 1 \%}$ reduction in MAPE compared to baselines with larger domains, i.e., plaintext space, while maintaining acceptable memory consumption.

Abstract This paper addresses the issue in live events, especially in sports viewing, where individuals who are deaf, hard of hearing (DHH), blind, or have low vision (BLV) struggle to access sufficient information. Traditionally, information accessibility has relied on specific professionals or volunteers, which is often inadequate. To tackle this challenge, we propose a mechanism that facilitates information sharing for all individuals, regardless of their abilities. We have developed an inclusive web application tailored to address these needs. This application is beneficial not only for DHH and BLV individuals but also for all audiences. Additionally, the system’s applicability extends to other domains, such as museum visits. This paper details the newly developed web application and presents the outcomes of pilot studies conducted in sports viewing and museum settings with DHH and BLV participants. The results of these experiments are analyzed to assess the system’s effectiveness and to identify future improvement areas.

In the big data era, cloud-based machine learning as a service (MLaaS) has attracted considerable attention. However, when handling sensitive data, such as financial and medical data, a privacy issue emerges, because the cloud server can access clients' raw data. A common method of handling sensitive data in the cloud uses homomorphic encryption, which allows computation over encrypted data without decryption. Previous research adopted a low-degree polynomial mapping function, such as the square function, for data classification. However, this technique results in low classification accuracy. This study seeks to improve the classification accuracy for inference processing in a convolutional neural network (CNN) while using homomorphic encryption. We apply various orders of the polynomial approximations of Google's Swish and ReLU activation functions. We also adopt batch normalization to normalize the inputs for the approximated activation functions to fit the input range to minimize the error. We implemented CNN inference labeling over homomorphic encryption using the Microsoft's Simple Encrypted Arithmetic Library (SEAL) for the Cheon-Kim-Kim-Song (CKKS) scheme. The experimental evaluations confirmed classification accuracies of 99.29% and 81.06% for MNIST and CIFAR-10, respectively, which entails 0.11% and 4.69% improvements, respectively, over previous methods.

Smart computing aims to improve the quality of life by utilizing Internet-of-Things devices and cloud computing. Typically, this computing handles private and/or personal information so concealing such sensitive information is a challenge. Adopting fully homomorphic encryption (FHE) is one approach for handling such sensitive information safely; that is, we can calculate the encrypted data without decryption. However, the time and space complexity of the FHE operation is high. Thus, its computation takes a long time. In this study, we aim to shorten FHE execution time by adopting our new scheduling algorithm, which divides a task into several macro-tasks and then assigns a set of threads. We assume a cloud computing system that is equipped with a many-core CPU. Thus, we propose the dynamic allocation method of computation resource to macro-tasks (DAMCREM), which dynamically allocates a certain number of threads (selected from pre-defined candidates) to each macro-task of every given job. In the evaluation, we compared DAMCREM to naive methods that allocate a pre-defined number of threads to each macro-task. The result shows that the average latency and maximum latency of job execution is less than those of naive methods, even when the average interval of job arrival is short.

The preservation of privacy during a search has become a serious problem in recent years. There is an increasing requirement to ensure that user queries are not abused by a third party, including the search provider. Fully homomorphic encryption (FHE) can conduct addition and multiplication directly over ciphertext. Using FHE, privacy, concerning both the user queries and the database of the search provider, can be protected. In this paper, we propose a privacy-preserving query system model. We implemented the proposed model in a real-world medicine side-effect query system. We applied a filtering technique, prior to the query deployment, to reduce the size of the database and used multi-threading to accelerate the search. The system was tested 10,000 times with a random query, using a database comprising 40,000 records of simulation data, and completed 99.84% of the queries within 60 seconds (s), proving the real-world applicability of our system.

In the era of big data and cloud computing, outsourcing data storage to the cloud poses the risk of its abuse or leakage. Thus, we address the problem of delegating computation on outsourced private datasets while maintaining privacy. In this study, we consider a scenario involving two data owners outsourcing their datasets to a cloud service. The cloud performs a set union computation, after which the querier sends a query to obtain information from both datasets. We propose a protocol that uses fully homomorphic encryption (FHE) and Cartesian-join of Bloom filters (CBF) as proposed by Wang et al. The protocol obtains information on the existence of a particular set of elements without learning about the residing source. To the best of our knowledge, our protocol, by using the FHE and CBF matrix, is a novel approach to ensuring the security of outsourced set union operations.