Management of secret keys for digital signatures is one of the most critical issues in decentralized applications. Since there is no administrator, losing a secret key can result in losing all assets or rights. To address this problem, fuzzy extractors and fuzzy signatures, which generate private keys directly from biometric information, have been considered in addition to conventional biometric authentication. However, these methods using biometric secret keys do not support group signatures. Therefore, it is not applicable to use cases that require consensus building by a specific community (group), such as DAO and DeFi.In this paper, we propose a new scheme for converting existing fuzzy signatures to group signatures using zero-knowledge proofs to address this problem. More precisely, we first define an anonymizable signature that is a generalization of a group signature and then convert a fuzzy signature into an anonymizable signature using an ordinary (classical) zero-knowledge proof. In addition, the signature data size is optimized to a constant size using zk-SNARK. Our implementation experiments show that our schemes achieve practical signature generation and verification times and signature sizes even for a group of up to 100,000 people. This paper’s results can be used to prevent the loss of secret keys and enable flexible DApps use cases.

Bug bounties are a helpful system for identifying vulnerabilities in smart contracts. Many web3 applications use bug bounties today. However, hackers might worry about not getting paid or receiving a reduced bounty if the bounty is not exchanged atomically with a bug report. This concern could lead hackers to exploit the identified bug instead of reporting it. Furthermore, other efforts related to bug bounties require modification of existing smart contracts. To solve this, we propose a practical bug bounty platform for smart contracts, ContractSafeguard, that utilizes Intel SGX to ensure the existence of bugs and atomic exchange of bounty and bug reports. Bug hunters send transactions to smart contracts hosted within the Enclave and glitch them. Test code within the Enclave generates a bug report that includes proof of the bug’s existence and a method that triggers the bug. In other words, within the Enclave, our mechanism detects and proves that the smart contract under test has entered an abnormal state. Our on-chain bug bounty contract then verifies the proof within the bug report and pays the bounty to the bug hunter atomically. Since the proof is produced within the Enclave, generating false proofs is impossible, and the on-chain contract can trust the report. Additionally, the developer can use existing smart contracts without modification since we have a runtime of smart contracts in the Enclave. We design ContractSafeguard for Ethereum smart contracts.

As transactions of monetary value on the public blockchain become more widespread, there is a growing demand that the transactions be verified as legitimate. As a result, it has become mandatory for crypto asset exchanges to perform identity verification. On the other hand, Bitcoin and other public blockchains are easy to start transactions with, and the anonymity of the transactions is a significant value for users. To achieve these conflicting requirements at a certain level, we developed a method to manage information generated from a user’s biometric information with smart contract and link transactions on the blockchain to real people. This mechanism makes it relatively easy for the user to claim their identity while the operator can control the degree of identity verification.

A Mnemonic phrase is a set of secret words that correspond one-to-one with the secret key of a user's crypto wallet. If this phrase is lost, the user cannot access the cryptocurrencies. Thus, the user wants to back up the phrase with multiple storage, but the risk of leakage increases. To solve this dilemma, this paper proposes a leakable mnemonic phrase system in which the phrase is encrypted with a secret key generated from the user's biometric information. Since the leakable mnemonic phrase can be decrypted only with the user's biometric information, it can be backed up to various external storage and devices to solve the contradictory problem of keeping the contents of the phrase secret while preventing its loss. In this paper, we also show the results of evaluating the performance of this system by implementing it on an actual biometric authentication device. The encryption/decryption processing time is fast (200 ms), and the system successfully decrypted the mnemonic phrase six months after enrollment.

Non-Fungible Token(NFT) issued on the blockchain made adding scarcity to digital data possible. However, as NFT transactions soared, illegal use of other people's content increased. To solve this situation, this paper proposes the Proof of Origin concept, which adds authenticity to NFTs by utilizing a technology that generates the cryptographic keys required for digital signatures directly from biometric information and describes the system architecture, data structure, and implementation of this concept. Using biometric information to link NFTs to actual persons makes it possible to provide trustworthiness and high-added value to data. At the same time, the creator does not need to manage secret keys and can safely and efficiently claim the originality of the data.

The use of database management systems (DBMSs) as a cloud service is rapidly expanding. Cloud DBMSs offer many advantages, such as easier management, lower costs, and greater scalability. However, there are still security concerns regarding attacks from adversaries. DBMSs that use searchable encryption have been investigated with regard to ensuring their security. Because searchable encryption allows query execution over encrypted data in the cloud, sensitive data can be securely stored there in the cloud. On the other hand, encrypted query processing is slower than query processing on plaintext data. In this article, we use a field-programmable gate array (FPGA) to accelerate query processing in a searchable encrypted DBMS. We also propose a new cache function to shorten the access time to database tables in a DBMS. According to an evaluation using basic queries, the proposed system has achieved up to 110.7 times speed-up compared with the central processing unit (CPU) processing of a single core. In addition, the proposed system can process queries faster than the plaintext processing on a CPU when processing large amounts of data.

Large demands for mobile traffic subject base stations to frequent short-term and sharp peak loads. Recent analysis of data traffic on commercial mobile networks reported that the traffic peaks can be reduced by an average of 40% without compromising the quality of experience provided to the end user, if a peak load can be shifted for at most 20 s. To reduce peak traffic, we previously proposed a method for off-peak data transfer, with which user equipment (UE) autonomously delays receiving data, and a peak load on a base station can be shifted. In terms of off-peak transfer of data, a significant problem is determining how each UE estimates available throughput. In this paper we propose a method of passively estimating available throughput of each UE. We evaluated the effectiveness of the proposed method through experiments on experimental and commercial LTE networks. The results indicate that our method obtains more than a 0.7 correlation between actual available throughput and estimated throughput.

In this paper, we propose an encrypted k–anonymization scheme (EAS) to k–anonymize an encrypted database using a domain generalized hierarchy while maintaining the encryption state. Preparation of the domain generalized hierarchy is optional; the proposed EAS can generate domain generalized hierarchies using a Huffman code tree from a database encrypted with searchable encryption. As a result, the user can delegate k–anonymization processing to a third party organization such as the cloud while retaining the confidentiality of the database without preparing a generalized hierarchy. In addition, third-party organizations that are entrusted also have the advantage to eliminate possible of misconduct such as information leakage. In a standard computer experiment, we performed a generalization process, which is the major procedure for our EAS. The generalization process takes around 168 seconds only to achieve k–anonymity with k = 3 on 1,000,000 records consisting of 4 attributes. As a consequence, this high-speed performance means our EAS is applicable to not only batch processing but also real-time processing.

The materials around us usually exist as mixtures of constituents, each constituent with possibly a different elasto-viscoplastic property. How can we describe the material property of such a mixture is the core question of this paper. We propose a nonlinear blending model that can capture intriguing flowing behaviors that can differ from that of the individual constituents (Fig. 1). We used a laboratory device, rheometer , to measure the flowing properties of various fluid-like foods, and found that an elastic Herschel-Bulkley model has nice agreements with the measured data even for the mixtures of these foods. We then constructed a blending model such that it qualitatively agrees with the measurements and is closed in the parameter space of the elastic Herschel-Bulkley model. We provide validations through comparisons between the measured and estimated properties using our model, and comparisons between simulated examples and captured footages. We show the utility of our model for producing interesting behaviors of various mixtures.

In recent years, Bitcoin, Ethereum and other cryptocurrencies have attracted a great deal of attention from the whole industry including the financial as a new settlement system. Transaction information of these cryptocurrencies is stored in a distribution ledger called Blockchain on the P2P network through processing such as PoW. Meanwhile, since PoW requires a large amount of computer resources, researches on private / consortium type blockchain that do not need PoW. In this paper, we propose a decentralized netting protocol using a consortium type block chain that has the channel function. On a system that implements the proposed protocol, netting settlement can be performed on P2P hiding information of the sender and receiver name of transaction, amount of money, calculation butt of netting, and without setting up a specific central organization such as a central server.

A method for off-peak transfer of data, by which user equipment (UE) autonomously shifts a peak load on a base station, is proposed and evaluated. In regard to the proposed method, how UE monitors the load on a base station is a significant problem. To solve this problem, a novel method of passively estimating the resource block (RB) usage rate of a serving cell on the basis of radio conditions measured by UE is presented. The estimated RB usage rate can be regarded as the traffic load on the base station. The effectiveness of the proposed methods for load monitoring and off-peak transfer of data was validated through experiments on commercial LTE networks.

The rapid growth in mobile data demands often drives the radio resource shortage of cells at a base station (BS). One of the most promising approaches to solve this problem is offpeak data transfer in which delay-insensitive data transfer is shifted to the low load interval of the serving cell. For shifting peaks effectively, an accurate cell load estimation is indispensable. However, active measurement of the load causes an additional network load and energy consumption of the user equipment (UE). Therefore, we have been investigating a cell load estimation method for an LTE base station from radio quality information measured passively by the UE. Analysis of a conventional estimation equation clarified that we should consider the configuration of the BS such as channel mapping and transmission power for every frequency block and measurement error of radio quality information in order to estimate the load accurately. In this paper, we propose an adaptable cell load estimation method based on BS configuration and verify the precision of our method through experiments using an LTE demo system.

Method names are important for the software development process. It has been shown by some studies that the quality of method names affects software comprehension. In response, some approaches that evaluate comprehensibility of method names have been proposed. However, the effectiveness of existing approaches is limited because they focus on part of names.

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