It appears that interest in the Internet of things (IoT) has recently reached its peak, with a great deal of focus from both the private and public sectors. IoT, a technology that enables the exchange of data through linkage among all objects surrounding the user, can create new services. Data communication among objects is not limited to personal information, but can also deliver different data types, such as sensing information collected from the surrounding environment. When such data is collected and used maliciously by an attacker, it is more vulnerable to threats than in conventional network environments. Security of all data transmitted in the IoT environment is therefore essential for preventing attacks. However, it is difficult to apply the conventional cipher algorithm to lightweight devices. Therefore, we propose a method for sharing and managing data using the conventional cipher algorithm on lightweight devices in various circumstances. This method implements proxy re-encryption in order to manage data with fewer encryptions, and provides a data sharing function to supplement the insufficient capacity of lightweight device networks.

In cloud computing environments, user data are encrypted using numerous distributed servers before storing such data. Global Internet service companies, such as Google and Yahoo, recognized the importance of Internet service platforms and conducted self-research and development to create and utilize large cluster-based cloud computing platform technology based on low-priced commercial nodes. As diverse data services become possible in distributed computing environments, high-capacity distributed management is emerging as a major issue. Meanwhile, because of the diverse forms of using high-capacity data, security vulnerability and privacy invasion by malicious attackers or internal users can occur. As such, when various sensitive data are stored in cloud servers and used from there, the problem of data spill might occur because of external attackers or the poor management of internal users. Data can be managed through encryption to prevent such problems. However, existing simple encryption methods involve problems associated with the management of access to data stored in cloud environments. Therefore, in the present paper, a technique for data access management by user authority, based on Attribute-Based Encryption (ABE) and secret distribution techniques, is proposed.

In a cloud computing environment, user data is encrypted and stored using a large number of distributed servers. Global Internet service companies such as Google and Yahoo have recognized the importance of Internet service platforms and conducted their own research and development to utilize large cluster-based cloud computing platform technologies based on low-cost commercial off-the-shelf nodes. Accordingly, as various data services are now allowed over a distributed computing environment, distributed management of big data has become a major issue. On the other hand, security vulnerability and privacy infringement due to malicious attackers or internal users can occur by means of various usage types of big data. In particular, various security vulnerabilities can occur in the block access token, which is used for the permission control of data blocks in Hadoop. To solve this problem, we have proposed a weight-applied XOR-based efficient distribution storage and recovery scheme in this paper. In particular, various security vulnerabilities can occur in the block access token, which is used for the permission control of data blocks in Hadoop. In this paper, a secret sharing-based block access token management scheme is proposed to overcome such security vulnerabilities.

Decentralized Identifiers have recently expanded into Internet of Things devices and are crucial in securing users’ digital identities and data. However, Decentralized Identifiers face challenges in scenarios necessitating authority delegation and anonymity, such as when dealing with legal guardianship for minors, device loss or damage, and specific medical contexts involving patient information. This paper aims to strengthen data sovereignty within the Decentralized Identifier system by implementing a secure authority delegation and anonymity scheme. It suggests optimizing verifiable presentations by utilizing a sequential aggregate signature, a Non-Interactive Zero-Knowledge Proof, and a Merkle tree to prevent against linkage and Sybil attacks while facilitating delegation. This strategy mitigates security risks related to delegation and anonymity, efficiently reduces the computational and verification efforts for signatures, and reduces the size of verifiable presentations by about 1.2 to 2 times.

Some artificial intelligence (AI) workflows rely on the integration of data from multiple distributed sources during the perception stage, making privacy-preserving data integration important when those sources contain private information. Private set union (PSU) enables the union of private datasets to be computed without directly revealing the underlying inputs. From a computer modeling perspective, agentic AI data integration can be represented as a multiparty process in which an AI system has no dataset of its own while obtaining an integrated dataset from distributed sources. However, conventional multiparty private set union (MPSU) protocols generally assume that all participating parties provide input sets, making them unsuitable for this inputless receiver setting. To address this gap, this paper proposes an MPSU protocol as a computational framework in which an inputless receiver obtains the union of private sets provided by multiple senders. The proposed scheme employs a star topology and tagged membership oblivious transfer (TMOT) to perform secure deduplication between the receiver and each sender, eliminating direct sender-to-sender communication during element deduplication. The security analysis shows that the scheme securely realizes the defined MPSU functionality under the semi-honest security model. The performance evaluation presents runtime and communication cost results and shows that the proposed scheme requires fewer inter-party communication rounds than the prior schemes under the criterion used in this paper. Consequently, this paper provides a practical computational model for privacy-preserving data integration in agentic AI systems.

In the IOT environment, network edges have data, but a centralized server may need to obtain the union from edges for efficient data access. PSU is a cryptographic primitive that allows protocol parties to compute the union of their private datasets without revealing any extra information. Traditional PSU protocols presuppose that all parties must input their private datasets. This assumption doesn’t hold in some scenarios where an inputless Third party needs to get the union. For instance, a regulatory organization may need to get the union of patient data from hospitals for statistical analysis, without inputting any dataset. We propose a novel TP-PSU, specifically designed for a setting with three parties and an inputless Third party. Our protocol enables the Third party to compute the union, while preventing the leakage of any other extra information. This includes protecting the origin and duplication of each data item across edge nodes, thus maintaining privacy in the IoT environment.