• Journal of Information Processing Systems
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• v.13 no.3
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• pp.573-589
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• 2017

Cloud computing is an attractive solution that can provide low cost storage and powerful processing capabilities for government agencies or enterprises of small and medium size. Yet the confidentiality of information should be considered by any organization migrating to cloud, which makes the research on relational database system based on encryption schemes to preserve the integrity and confidentiality of data in cloud be an interesting subject. So far there have been various solutions for realizing SQL queries on encrypted data in cloud without decryption in advance, where generally homomorphic encryption algorithm is applied to support queries with aggregate functions or numerical computation. But the existing homomorphic encryption algorithms cannot encrypt floating-point numbers. So in this paper, we present a mechanism to enable the trusted party to encrypt the floating-points by homomorphic encryption algorithm and partial trusty server to perform summation on their ciphertexts without revealing the data itself. In the first step, we encode floating-point numbers to hide the decimal points and the positive or negative signs. Then, the codes of floating-point numbers are encrypted by homomorphic encryption algorithm and stored as sequences in cloud. Finally, we use the data structure of DoubleListTree to implement the aggregate function of SUM and later do some extra processes to accomplish the summation.

• Journal of Information Processing Systems
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• v.20 no.4
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• pp.432-441
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• 2024

To improve the security of gene information and the accuracy of matching, this paper designs a homomorphic encryption algorithm for gene matching based on cloud computing environment. Firstly, the gene sequences of cloud files entered by users are collected, which are converted into binary code by binary function, so that the encrypted text is obviously different from the original text. After that, the binary code of genes in the database is compared with the generated code to complete gene matching. Experimental analysis indicates that when the number of fragments in a 1 GB gene file is 65, the minimum encryption time of the algorithm is 80.13 ms. Aside from that, the gene matching time and energy consumption of this algorithm are the least, which are 85.69 ms and 237.89 J, respectively.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.3
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• pp.375-381
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• 2023

Fully homomorphic encryption enables algebraic operations on encrypted data, and recently, methods for approximating non-algebraic operations such as the maximum function have been studied. However, precise approximation of max-pooling operations for four or more numbers have not been researched yet. In this study, we propose a precise max-pooling approximation method using the composition of approximate polynomials of the maximum function and theoretically analyze its precision. Experimental results show that the proposed approximate max-pooling has a small amortized runtime of less than 1ms and high precision that matches the theoretical analysis.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.5
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• pp.865-873
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• 2024

In this study, we propose a privacy-preserving cryptographic API misuse detection framework utilizing homomorphic encryption. The proposed framework is designed to effectively detect cryptographic API misuse while maintaining data confidentiality. We employ a Convolutional Neural Network (CNN)-based detection model and optimize its structure to ensure high accuracy even in an encrypted environment. Specifically, to enable efficient homomorphic operations, we leverage depth-wise convolutional layers and a cubic activation function to secure non-linearity, enabling effective misuse detection on encrypted data. Experimental results show that the proposed model achieved a high F1-score of 0.978, and the total execution time for the homomorphically encrypted model was 11.20 seconds, demonstrating near real-time processing efficiency. These findings confirm that the model offers excellent security and accuracy even when operating in a homomorphic encryption environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.3
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• pp.1510-1532
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• 2017

In smart grid, privacy implications to individuals and their families are an important issue because of the fine-grained usage data collection. Wireless communications are utilized by many utility companies to obtain information. Network coding is exploited in smart grids, to enhance network performance in terms of throughput, delay, robustness, and energy consumption. However, random linear network coding introduces a new challenge for privacy preserving due to the encoding of data and updating of coefficients in forwarder nodes. We propose a distributed privacy preserving scheme for random linear network coding in smart grid that considers the converged flows character of the smart grid and exploits a homomorphic encryption function to decrease the complexities in the forwarder node. It offers a data confidentiality privacy preserving feature, which can efficiently thwart traffic analysis. The data of the packet is encrypted and the tag of the packet is encrypted by a homomorphic encryption function. The forwarder node random linearly codes the encrypted data and directly processes the cryptotext tags based on the homomorphism feature. Extensive security analysis and performance evaluations demonstrate the validity and efficiency of the proposed scheme.

• Journal of Internet Technology
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• v.22 no.6
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• pp.1287-1297
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• 2021

Data aggregation is the significant process in which the information is gathered and combines data to decrease the amount of data transmission in the WSN. The sensor devices are susceptible to node attacks and security issues such as data confidentiality and data privacy are extremely important. A novel technique called Ruzicka Index Regressive Homomorphic Ephemeral Key Benaloh Cryptography (RIRHEKBC) technique is introduced for enhancing the security of data aggregation and data privacy in WSN. By applying the Ruzicka Index Regressive Homomorphic Ephemeral Key Benaloh Cryptography, Ephemeral private and public keys are generated for each sensor node in the network. After the key generation, the sender node performs the encryption using the receiver public key and sends it to the data aggregator. After receiving the encrypted data, the receiver node uses the private key for decrypting the ciphertext. The key matching is performed during the data decryption using Ruzicka Indexive regression function. Once the key is matched, then the receiver collects the original data with higher security. The simulation result proves that the proposed RIRHEKBC technique increases the security of data aggregation and minimizes the packet drop, and delay than the state-of-the- art methods.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.60-66
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• 2021

Blockchain is a technology that enables trust-based consensus and verification based on a decentralized network. Distributed ID (DID) is based on a decentralized structure, and users have the right to manage their own ID. Recently, interest in self-sovereign identity authentication is increasing. In this paper, as a method for transparent and safe sovereignty management of data, among data pseudonymization techniques for blockchain use, various methods for data encryption processing are examined. The public key technique (homomorphic encryption) has high flexibility and security because different algorithms are applied to the entire sentence for encryption and decryption. As a result, the computational efficiency decreases. The hash function method (MD5) can maintain flexibility and is higher than the security-related two-way encryption method, but there is a threat of collision. Zero-knowledge proof is based on public key encryption based on a mutual proof method, and complex formulas are applied to processes such as personal identification, key distribution, and digital signature. It requires consensus and verification process, so the operation efficiency is lowered to the level of O (log_eN) ~ O(N²). In this paper, data encryption processing for blockchain DID, based on zero-knowledge proof, was proposed and a one-way encryption method considering data use range and frequency of use was proposed. Based on the content presented in the thesis, it is possible to process corrected zero-knowledge proof and to process data efficiently.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.3
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• pp.413-426
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• 2017

In this paper, we propose an adder that can be applied to data encrypted with a fully homomorphic encryption scheme and an addition method with improved performance that can be applied when adding multiple data. The proposed arithmetic adder is based on the Kogge-Stone Adder method with the optimal circuit level among the existing hardware-based arithmetic adders and suitable to apply the cryptographic SIMD (Single Instruction for Multiple Data) function on encrypted data. The proposed multiple addition method does not add a large number of data by repeatedly using Kogge-Stone Adder which guarantees perfect addition result. Instead, when three or more numbers are to be added, three numbers are added to C (Carry-out) and S (Sum) using the full-adder circuit implementation. Adding with Kogge-Stone Adder is only when two numbers are finally left to be added. The performance of the proposed method improves dramatically as the number of data increases.

• Annual Conference of KIPS
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• 2023.05a
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• pp.123-125
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• 2023

As deep learning has become an essential part of human lives, the requirement for Deep Learning as a Service (DLaaS) is growing. Since using remote cloud servers induces privacy concerns for users, a Fully Homomorphic Encryption (FHE) arises to protect users' sensitive data from a malicious attack in the cloud environment. However, the FHE cannot support several computations, including the most popular activation function, Rectified Linear Unit (ReLU). This paper analyzes several polynomial approximation methods for ReLU to utilize FHE in DLaaS.

• Journal of Information Processing Systems
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• v.20 no.1
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• pp.116-130
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• 2024

In the Internet-of-Things (IoT) or blockchain-based network systems, secure keys may be stored in individual devices; thus, individual devices should protect data by performing secure operations on the data transmitted and received over networks. Typically, secure functions, such as a physical unclonable function (PUF) and fully homomorphic encryption (FHE), are useful for generating safe keys and distributing data in a network. However, to provide these functions in embedded devices for IoT or blockchain systems, proper inspection is required for designing and implementing embedded system-on-chip (SoC) modules through overhead and performance analysis. In this paper, a virtual platform (SoC VP) was developed that includes a secure key generation module with a PUF and FHE. The SoC VP platform was implemented using SystemC, which enables the execution and verification of various aspects of the secure key generation module at the electronic system level and analyzes the system-level execution time, memory footprint, and performance, such as randomness and uniqueness. We experimentally verified the secure key generation module, and estimated the execution of the PUF key and FHE encryption based on the unit time of each module.