Digital twin technology is recently in the spotlight because of its potential applicability in business and industry. In digital twin environments, data generated from physical assets are transmitted to a remote server, which performs simulations through digital twins in a virtual space. Then, the simulation results can be shared with the data owner or other users. However, several challenges exist in the application of digital twin technology in the real world. One of the main challenges involves determining how to share real-time data for the simulation and how to share the simulation data securely. The data generated from physical assets may include sensitive information from data owners, and the leakage of data to an adversary can cause serious privacy problems. Moreover, the sharing of data with other data users should also be considered to maximize the availability of digital twin data. To resolve these issues, we propose a system model for the secure sharing of digital twin data. The proposed system model uses cloud computing for efficient data sharing and blockchain for data verifiability. We also propose communication schemes for the proposed model to guarantee privacy preservation and data security in wireless channels. We analyze the security of the proposed protocol using informal methods and formal methods such as BAN logic and the AVISPA simulation tool. Furthermore, we compare the proposed protocol with related protocols and demonstrate that the proposed scheme is applicable to digital twin environments.

Smart homes are an emerging paradigm of Internet of Things (IoT) in which users can remotely control various home devices via the internet anytime and anywhere. However, smart home environments are vulnerable to security attacks because an attacker can inject, insert, intercept, delete, and modify transmitted messages over an insecure channel. Thus, secure and lightweight authentication protocols are essential to ensure useful services in smart home environments. In 2021, Kaur and Kumar presented a two-factor based user authentication protocol for smart homes using elliptic curve cryptosystems (ECC). Unfortunately, we demonstrate that their scheme cannot resist security attacks such as impersonation and session key disclosure attacks, and also ensure secure user authentication. Moreover, their scheme is not suitable in smart home environments because it utilizes public-key cryptosystems such as ECC. Hence, we design a secure and lightweight three-factor based privacy-preserving authentication scheme for IoT-enabled smart home environments to overcome the security problems of Kaur and Kumar’s protocol. We prove the security of the proposed scheme by using informal and formal security analyses such as the ROR model and AVISPA simulation. In addition, we compare the performance and security features between the proposed scheme and related schemes. The proposed scheme better provides security and efficiency compared with the previous schemes and is more suitable than previous schemes for IoT-enabled smart home environments.

Traditional public key exchange protocols are based on algebraic number theory. In another perspective, neural cryptography, which is based on neural networks, has been emerging. It has been reported that two parties can exchange secret key pairs with the synchronization phenomenon in neural networks. Although there are various models of neural cryptography, called Tree Parity Machine (TPM), many of them are not suitable for practical use, considering efficiency and security. In this paper, we propose a Vector-Valued Tree Parity Machine (VVTPM), which is a generalized architecture of TPM models and can be more efficient and secure for real-life systems. In terms of efficiency and security, we show that the synchronization time of the VVTPM has the same order as the basic TPM model, and it can be more secure than previous results with the same synaptic depth.

Currently, “connected cars” are being actively designed over smart cars and autonomous cars, to establish a two-way communication network between the vehicle and all infrastructure. Additionally, because vehicle black boxes are becoming more common, specific processes for secure and efficient data sharing and transaction via vehicle networks must be developed. In this paper, we propose a Blockchain-based vehicle data marketplace platform model, along with a data sharing scheme, using Blockchain-based data-owner-based attribute-based encryption (DO-ABE). The proposed model achieves the basic requirements such as data confidentiality, integrity, and privacy. The proposed system securely and effectively handles large-capacity and privacy-sensitive black box video data by storing the metadata on Blockchain (on-chain) and encrypted raw data on off-chain (external) storage, and adopting consortium Blockchain. Furthermore, the data owners of the proposed model can control their own data by applying the Blockchain-based DO-ABE and owner-defined access control lists.

In IoT-based environments, smart services can be provided to users under various environments, such as smart homes, smart factories, smart cities, smart transportation, and healthcare, by utilizing sensing devices. Nevertheless, a series of security problems may arise because of the nature of the wireless channel in the Wireless Sensor Network (WSN) for utilizing IoT services. Authentication and key agreements are essential elements for providing secure services in WSNs. Accordingly, two-factor and three-factor-based authentication protocol research is being actively conducted. However, IoT service users can be vulnerable to ID/password pair guessing attacks by setting easy-to-remember identities and passwords. In addition, sensors and sensing devices deployed in IoT environments are vulnerable to capture attacks. To address this issue, in this paper, we analyze the protocols of Chunka et al., Amintoosi et al., and Hajian et al. and describe their security vulnerabilities. Moreover, this paper introduces PUF and honey list techniques with three-factor authentication to design protocols resistant to ID/password pair guessing, brute-force, and capture attacks. Accordingly, we introduce PUFTAP-IoT, which can provide secure services in the IoT environment. To prove the security of PUFTAP-IoT, we perform formal analyses through Burrows Abadi Needham (BAN) logic, Real-Or-Random (ROR) model, and scyther simulation tools. In addition, we demonstrate the efficiency of the protocol compared with other authentication protocols in terms of security, computational cost, and communication cost, showing that it can provide secure services in IoT environments.

Fault attacks (FA) intentionally inject some fault into the encryption process for analyzing a secret key based on faulty intermediate values or faulty ciphertexts. One of the easy ways for software-based countermeasures is to use time redundancy. However, existing methods can be broken by skipping comparison operations or by using non-uniform distributions of faulty intermediate values. In this paper, we propose a secure software-based redundancy, aptly named table redundancy, applying different linear and nonlinear transformations to redundant computations of table-based block cipher structures. To reduce the table size and the number of lookups, some outer tables that are not subjected to FA are shared, while the inner tables are protected by table redundancy. The basic idea is that different transformations protecting redundant computations are correctly decoded if the redundant outcomes are combined without faulty values. In addition, this recombination provides infective computations because a faulty byte is likely to propagate its error to adjacent bytes due to the use of 32-bit linear transformations. Our method also presents a stateful feature in the connection with detected faults and subsequent plaintexts for preventing iterative fault injection. We demonstrate the protection of AES-128 against FA and show a negligible advantage of FA.