A structured review of lattice based attribute based encryption methods for post quantum security

Most data nowadays are stored in the cloud; therefore, cloud computing and its extension—fog computing—are the most in-demand services at the present time. Cloud and fog computing platforms are largely used by Internet of Things (IoT) applications where various mobile devices, end users, PCs, and smart objects are connected to each other via the internet. IoT applications are common in several application areas, such as healthcare, smart cities, industries, logistics, agriculture, and many more. Due to this, there is an increasing need for new security and privacy techniques, with attribute-based encryption (ABE) being the most effective among them. ABE provides fine-grained access control, enables secure storage of data on unreliable storage, and is flexible enough to be used in different systems. In this paper, we survey ABE schemes, their features, methodologies, benefits/drawbacks, attacks on ABE, and how ABE can be used with IoT and its applications. This survey reviews ABE models suitable for IoT platforms, taking into account the desired features and characteristics. We also discuss various performance indicators used for ABE and how they affect efficiency. Furthermore, some selected schemes are analyzed through simulation to compare their efficiency in terms of different performance indicators. As a result, we find that some schemes simultaneously perform well in one or two performance indicators, whereas none shines in all of them at once. The work will help researchers identify the characteristics of different ABE schemes quickly and recognize whether they are suitable for specific IoT applications. Future work that may be helpful for ABE is also discussed.

Functional Encryption denotes a form of encryption where a master secret key-holder can control which functions a user can evaluate on encrypted data. Learning With Errors (LWE) (Regev, STOC’05) is known to be a useful cryptographic hardness assumption which implies strong primitives such as, for example, fully homomorphic encryption (Brakerski-Vaikuntanathan, FOCS’11) and lockable obfuscation (Goyal et al., Wichs et al., FOCS’17). Despite its stre ngth, however, there is just a limited number of functional encryption schemes which can be based on LWE. In fact, there are functional encryption schemes which can be achieved by using pairings but for which no secure instantiations from lattice-based assumptions are known: function-hiding inner product encryption (Lin, Baltico et al., CRYPTO’17) and compact quadratic functional encryption (Abdalla et al., CRYPTO’18). This raises the question whether there are some mathematical barriers which hinder us from realizing function-hiding and compact functional encryption schemes from lattice-based assumptions as LWE. To study this problem, we prove an impossibility result for function-hiding functional encryption schemes which meet some algebraic restrictions at ciphertext encryption and decryption. Those restrictions are met by a lot of attribute-based, identity-based and functional encryption schemes whose security stems from LWE. Therefore, we see our results as important indications why it is hard to construct new functional encryption schemes from LWE and which mathematical restrictions have to be overcome to construct secure lattice-based functional encryption schemes for new functionalities.

In an attribute-based encryption (ABE) scheme, a ciphertext is associated with an l-bit public index pind and a message m, and a secret key is associated with a Boolean predicate P. The secret key allows to decrypt the ciphertext and learn m iff P(pind) = 1. Moreover, the scheme should be secure against collusions of users, namely, given secret keys for polynomially many predicates, an adversary learns nothing about the message if none of the secret keys can individually decrypt the ciphertext.We present attribute-based encryption schemes for circuits of any arbitrary polynomial size, where the public parameters and the ciphertext grow linearly with the depth of the circuit. Our construction is secure under the standard learning with errors (LWE) assumption. Previous constructions of attribute-based encryption were for Boolean formulas, captured by the complexity class NC1.In the course of our construction, we present a new framework for constructing ABE schemes. As a by-product of our framework, we obtain ABE schemes for polynomial-size branching programs, corresponding to the complexity class LOGSPACE, under quantitatively better assumptions.

One of the main aspects of this study was the use of Big Data tools to analyze and effectively use the data generated by Internet of Things (IoT) devices. Big data allows you to store and analyze a huge amount of diverse data, as well as to obtain valuable information from it. With the help of Big Data tools, it is possible to process this data efficiently using parallel algorithms and analysis methods in real or quasi-real time. The purpose of the article is to analyze the latest relevant research in the field of Big Data and the Internet of Things, as well as to reveal the features of their interdependence. To achieve this goal, the method of systematic review of scientific literature was used, which allowed to systematically trace and analyze significant research and publications in this area. The methodology of this research is based on a retrospective analysis of literature sources and scientific publications related to the use of Big Data and the Internet of Things in various fields. To collect data, a systematic search of electronic databases such as IEEE Xplore, ScienceDirect, and searches on web resources such as Google Scholar were used. The analysis identified key concepts, methods, and practical applications of combining Big Data and the Internet of Things. The latest developments in the Internet of Things architecture and Big Data models were analyzed. The practical significance of the study is that it contributes to a deeper understanding of the latest research in the areas of Big Data and the Internet of Things, the interaction between them and the identification of new opportunities for using this combination in various industries. The results of this research can be used to develop new technologies and improve existing systems for collecting, processing, and analyzing data. In addition, these findings can be used as a basis for further research in this area aimed at improving and implementing new solutions. This study helps to deepen the understanding of the potential of combining Big Data and the Internet of Things and to point out ways for further development in this area

Recent developments in cloud computing allow data to be securely shared between users. This can be used to improve the quality of life of patients and medical staff in the Internet of Medical Things (IoMT) environment. However, in the IoMT cloud environment, there are various security threats to the patient’s medical data. As a result, security features such as encryption of collected data and access control by legitimate users are essential. Many studies have been conducted on access control techniques using ciphertext-policy attribute-based encryption (CP-ABE), a form of attribute-based encryption, among various security technologies and studies are underway to apply them to the medical field. However, several problems persist. First, as the secret key does not identify the user, the user may maliciously distribute the secret key and such users cannot be tracked. Second, Attribute-Based Encryption (ABE) increases the size of the ciphertext depending on the number of attributes specified. This wastes cloud storage, and computational times are high when users decrypt. Such users must employ outsourcing servers. Third, a verification process is needed to prove that the results computed on the outsourcing server are properly computed. This paper focuses on the IoMT environment for a study of a CP-ABE-based medical data sharing system with key abuse prevention and verifiable outsourcing in a cloud environment. The proposed scheme can protect the privacy of user data stored in a cloud environment in the IoMT field, and if there is a problem with the secret key delegated by the user, it can trace a user who first delegated the key. This can prevent the key abuse problem. In addition, this scheme reduces the user’s burden when decoding ciphertext and calculates accurate results through a server that supports constant-sized ciphertext output and verifiable outsourcing technology. The goal of this paper is to propose a system that enables patients and medical staff to share medical data safely and efficiently in an IoMT environment.

IoT-friendly, pre-computed and outsourced attribute based encryption

Currently, the security situation of data security and user privacy protection is increasingly serious in cloud environment. Ciphertext data storage can prevent the risk of user's privacy disclosure. But how to search keyword on ciphertext data without revealing keyword information becomes a new challenge. Searchable encryption (SE) is put forward for this reason, which can be used to realize ciphertext-search directly. In terms of multi-user data sharing, public-key encryption with keywords search (PEKS) is more widely used than symmetric searchable encryption. PEKS has been widely studied and developed by researchers in recent years. However, the existing PEKS scheme often lacks flexible access policy. Therefore, combining the advantage of policy control on attribute-based encryption (ABE), and as that the bilinear pairing related assumption is fragile in post-quantum era, lattice-based cryptography is considered as one of secure encryption technology against quantum attack. With this background, in this paper, we give a keyword-searchable ABE scheme based on the hardness of lattice problems, our scheme supports flexible attribute control policy by integrating ABE and PEKS, and the security of new scheme is proved under the learning with errors (LWE) assumption. As lattice-based cryptographic technology is currently thought to be resistant to quantum attacks, so the new scheme has stronger security in a quantum era.

Internet of Things (IoT) has gripped domains with this ubiquitous connectivity, in-themoment data collection, and autonomous decision-making. But rising numbers of heterogeneous, extremely constrained IoT devices pose serious concerns regarding data privacy, security, and trust management, drawing great attention into these areas in the academic field and on all sides. Thus, blockchain technology came into the limelight for strengthening security and privacy in IoT systems in a decentralized manner, giving the system immutability, transparency, and distributed trust. This study proposes a Systematic Literature Review (SLR) of blockchain-based approaches that aim to enhance the IoT applications' privacy and security, focusing chiefly on healthcare, supply chains, and smart cities. The review uses a structured methodology to find, select, evaluate, and synthesize relevant peer-reviewed studies published between 2018 and 2025 taken from major scientific databases such as IEEE Xplore, ACM Digital Library, ScienceDirect, SpringerLink, and Scopus. Articles were also examined to narrow the scope of study and set the subject. The selected studies are analyzed and classified based on their security goals (e.g., confidentiality, integrity, authentication), privacy-preserving techniques (e.g., anonymization, differential privacy, zero-knowledge proofs), blockchain configurations (e.g., public, private, consortium), and consensus mechanisms. The findings reveal a growing body of research applying blockchain to a wide range of IoT domains, addressing diverse application domains such as healthcare, smart homes, industrial IoT, and agriculture, and demonstrating its potential to enhance data integrity, access control, and authentication. However, the integration of blockchain in IoT also faces challenges such as scalability, latency, and resource overhead, especially in real-time and constrained environments. This review offers a comprehensive synthesis of the state-of-the-art, identifies current limitations and research gaps, and proposes future research directions for building secure, efficient, privacy-aware, and scalable blockchain-enabled IoT systems.

Lightweight devices, such as a smartcard associated with a top‐box decoder in pay‐TV or a SIM card coupled with a powerful (but not totally trusted) smartphone, play an important role in modern applications. The essential requirements for a cryptographic scheme to be truly implemented in lightweight devices are that it should have compact secret key size and support fast decryption. Attribute‐based broadcast encryption (ABBE) combines the functionalities of both broadcast encryption and attribute‐based encryption in an efficient way, ABBE is therefore a promising cryptographic scheme to be used in practical applications such as mobile pay‐TV, satellite transmission, or Internet of Things. Designing an ABBE scheme which can be truly implemented in lightweight devices is still an open question. In this study, the authors solve it by proposing an efficient constant‐size private key ciphertext‐policy ABBE scheme for disjunctive normal form supporting fast decryption and achieving standard security levels of an ABBE scheme. They concretely show that the authors’ scheme can be truly implemented in a prototype for a smartphone‐based cloud storage use case. In particular, they show how to alleviate some parts of their scheme so as to obtain a very practical system, and they give some concrete benchmarks.

A lightweight attribute-based encryption scheme for the Internet of Things

The Internet of Things (IoT) is enabling a new generation of innovative services based on the seamless integration of smart objects into information systems. Such IoT devices generate an uninterrupted flow of information that can be transmitted through an untrusted network and stored on an untrusted infrastructure. The latter raises new security and privacy challenges that require novel cryptographic methods. Attribute-Based Encryption (ABE) is a new type of public-key encryption that enforces a fine-grained access control on encrypted data based on flexible access policies. The feasibility of ABE adoption in fully-fledged computing systems, i.e. smartphones or embedded systems, has been demonstrated in recent works. In this paper we assess the feasibility of the adoption of ABE in typical IoT constrained devices, characterized by limited capabilities in terms of computing, storage and power. Specifically, an implementation of three ABE schemes for ESP32, a low-cost popular platform to deploy IoT devices, is developed and evaluated in terms of encryption/decryption time and energy consumption. The performance evaluation shows that the adoption of ABE on constrained devices is feasible, although it has a cost that increases with the number of attributes. The analysis in particular highlights how ABE has a significant impact in the lifetime of battery-powered devices, which is impaired significantly when a high number of attributes is adopted.

Internet of Things (IoT) environments are widely employed in industrial applications including intelligent transportation systems, healthcare systems, and building energy management systems. For such environments of highly sensitive data, adapting scalable and flexible communication with efficient security is vital. Research investigated wireless Ad-hoc/mesh networking, while Attribute Based Encryption (ABE) schemes have been highly recommended for IoT. However, a combined implementation of both mesh networking and Key-Policy Attribute Based Encryption (KPABE) on resource-constrained devices has been rarely addressed. Hence, in this work, an integrated system that deploys a lightweight KPABE security built on wireless mesh networking is proposed. Implementation results show that the proposed system ensures flexibility and scalability of self-forming and cooperative mesh networking in addition to a fine-grained security access structure for IoT nodes. Moreover, the work introduces a case study of an enabled scenario at a school building for optimizing energy efficiency, in which the proposed integrated system architecture is deployed on IoT sensing and actuating devices. Therefore, the encryption attributes and access policy are well-defined, and can be adopted in relevant IoT applications.

New generation of connected healthcare systems has emerging digital technologies and medical science. They are converging more and more with time. This development delivers healthcare: it connects smart medical devices, analysis real-time data, and executes the make an advanced clinical decision. This progress creates more attached, efficient, and patient-centered healthcare ecosystem. With recent advancements in Internet of Things (IoT), continuous monitoring became possible. As well as predictive analytics and patient-centered care. On the other hand, with possible injury directly implicating patient safety and trust, growing requirement on Health-IoT (H-IoT) has carried critical security and privacy concerns. This chapter presents a brief overview of the H-IoT applied-systems. Device classification and architectural layers are also described. As well, fundamental security challenges are including: weak authentication, data integrity risks, and supply chain vulnerabilities. Then, real-world incidents and attacks in a infusion pump is discussed, following by an example of pacemaker recalls. Indeed, regulatory and ethical dimensions of H-IoT are discussed. Additionally, this chapter a survey reveals emerging solutions, such as blockchain, lightweight cryptography, zero-trust architectures, and privacy-preserving analytics and post-quantum security. Finally, this chapter concludes by some future oriented new IoT healthcare security.

With the rapid growth of Internet of Things (IoT) applications, fog computing enables the IoT to provide efficient services by extending the cloud computing paradigm to the edge of the network. However, the existing attribute-based encryption schemes rarely focus on user and attribute revocation in fog computing and most of them still impose high computational and storage overhead on resource-limited IoT devices. In this article, we propose an efficient attribute-based encryption outsourcing scheme with user and attribute revocation for fog-enabled IoT. The proposed scheme improves the existing encryption scheme that uses the concept of attribute groups to achieve attribute revocation, making it suitable for fog computing and improving the efficiency of ciphertext update. In addition, it implements a novel method of user revocation in fog computing based on the characteristics of fog computing. In order to reduce the computing and storage burden of IoT devices, the heavy computation operations of encryption and decryption are outsourced to fog nodes and part of secret keys are stored in fog nodes. The security analysis shows that the proposed scheme is secure under the Decisional Bilinear Diffie–Hellman (DBDH) assumption. The performance analysis shows that the proposed scheme has high revocation efficiency and is efficient enough to be deployed in practical fog computing.

LBAC: A lightweight blockchain-based access control scheme for the internet of things