Content-Aware Secure Search Using Attribute Based Encryption IoT Cloud Platforms

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.

The rapid urbanization and proliferation of Internet of Things (IoT) devices in smart cities generate massive data volumes, raising critical challenges in real-time processing, security, privacy, trust, and energy consumption. This work proposes a secure and sustainable AIoT (Artificial Intelligence of Things) architecture that synergistically integrates edge intelligence, blockchain, and green computing principles to address these issues holistically. Edge intelligence enables low-latency, distributed AI inference at the network periphery, reducing cloud dependency, bandwidth usage, and response times for latency-sensitive applications such as traffic management, environmental monitoring, and emergency response. Blockchain provides decentralized trust, immutability, and tamper-resistant data sharing through smart contracts, ensuring secure authentication, access control, and transparent transactions among heterogeneous IoT devices while mitigating single-point-of-failure risks. To promote sustainability, green computing strategies—including energy-efficient edge node design, renewable-powered hardware, dynamic resource allocation, and low-power protocolsare embedded to minimize carbon footprint and operational energy demands in resource-constrained environments. The proposed layered architecture (perception, edge AI processing, blockchain-secured middleware, and application layers) is evaluated through simulation and prototype implementation, demonstrating up to 40–60% reduction in latency and energy consumption compared to traditional cloud-centric models, alongside enhanced security against common threats (e.g., DDoS, data tampering). Results highlight improved scalability, resilience, and environmental efficiency, making this framework suitable for next-generation sustainable smart cities. This integrated approach paves the way for trustworthy, eco-friendly urban ecosystems, aligning with global sustainability goals (e.g., UN SDGs 9, 11, 13) and offering practical insights for deployment in real-world smart city initiatives

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.

Access control in Internet-of-Things: A survey

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

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.

The integration of Blockchain technology into Internet of Things (IoT) ecosystems has emerged as a transformative approach to address critical security and privacy challenges in connected devices. This chapter explores the innovative convergence of Blockchain and IoT, focusing on the enhancement of data security, privacy, and trust through decentralized solutions. By leveraging the immutable and transparent nature of Blockchain, IoT networks can achieve secure, efficient, and automated data exchanges. The chapter delves into key Blockchain trends that are shaping IoT security, including decentralized identity management, smart contracts, and consensus mechanisms, while addressing the specific challenges of resource-constrained IoT devices. The use of advanced cryptographic techniques such as encryption, Zero-Knowledge Proofs, and homomorphic encryption ensures that sensitive data remains private and secure. The application of Blockchain in facilitating secure data sharing, device authentication, and access control was discussed, alongside the potential of permissioned blockchains to balance privacy with scalability. The chapter concludes by identifying the future directions for Blockchain and IoT innovations, emphasizing their role in the evolution of secure, autonomous, and scalable connected environments.

The Internet of Things (IoT) is receiving considerable attention from both industry and academia because of the new business models that it enables and the new security and privacy challenges that it generates. Major Cloud Service Providers (CSPs) have proposed platforms to support IoT by combining cloud and edge computing. However, the security mechanisms available in the cloud have been extended to IoT with some shortcomings with respect to the management and enforcement of access control policies. Access Control as a Service (ACaaS) is emerging as a solution to overcome these difficulties. The paper proposes a lazy approach to ACaaS that allows the specification and management of policies independently of the CSP while leveraging its enforcement mechanisms. We demonstrate the approach by investigating (also experimentally) alternative deployments in the IoT platform offered by Amazon Web Services on a realistic smart lock solution.

The rapid growth of Internet of Things (IoT)-enabled smart home environments has amplified the need for robust, scalable, and context-aware access control mechanisms. Traditional Role-Based Access Control (RBAC) and Attribute-Based Access Control (ABAC) models offer distinct advantages such as hierarchical simplicity and contextual granularity, respectively, yet exhibit critical limitations when deployed in isolation. To address these constraints, this study proposes and implements a discrete, hybrid RBAC–ABAC access control framework, engineered as a smart contract on the Binance Smart Chain (BSC), to ensure a secure, decentralized model for managing access to IoT resources. The framework leverages blockchain’s inherent strengths cryptographic integrity, immutable message digests, and tamper-resistant audit trails to reinforce data confidentiality and policy enforcement. Validated on a real-world testbed comprising ESP32-based IoT devices, a Raspberry Pi gateway, and a blockchain backend, the proposed system demonstrates enhanced resilience and trust. Quantitative evaluations benchmark the hybrid model against standalone RBAC and ABAC mechanisms across key metrics, including gas consumption, access latency, access decision accuracy, and scalability. The results indicate the hybrid model achieves superior accuracy (97.2%) and scalability (supporting 50 concurrent users and 30 devices), with moderate gas usage (15,000 gwei) and acceptable latency (1.8 seconds). Furthermore, the use of laser-secure transaction protocols and cryptographic primitives within the smart contract enhances the protection of access rights and resource interactions. This hybrid smart contract architecture not only bridges the gap between theoretical access control models and practical blockchain deployments but also sets a foundation for adaptive, secure, and decentralized policy enforcement in next-generation smart homes, particularly in resource-constrained environments.

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

Research Article| March 01, 2016 The Internet of Geophysical Things: Raspberry Pi Enhanced REF TEK (RaPiER) System Integration and Evaluation Frank Sepulveda; Frank Sepulveda aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Search for other works by this author on: GSW Google Scholar Jay Pulliam Jay Pulliam aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Search for other works by this author on: GSW Google Scholar Author and Article Information Frank Sepulveda aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Jay Pulliam aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Publisher: Seismological Society of America First Online: 14 Jul 2017 Online Issn: 1938-2057 Print Issn: 0895-0695 © 2016 by the Seismological Society of America Seismological Research Letters (2016) 87 (2A): 345–357. https://doi.org/10.1785/0220150234 Article history First Online: 14 Jul 2017 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Frank Sepulveda, Jay Pulliam; The Internet of Geophysical Things: Raspberry Pi Enhanced REF TEK (RaPiER) System Integration and Evaluation. Seismological Research Letters 2016;; 87 (2A): 345–357. doi: https://doi.org/10.1785/0220150234 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietySeismological Research Letters Search Advanced Search ABSTRACT The proliferation of commercial Internet of Things (IoT) devices is raising consumers’ awareness of the benefits of enhancing everyday objects with the ability to communicate, sense, and process information. Commercial‐off‐the‐shelf (COTS) versions of the embedded technologies responsible for the rise of the IoT are easy‐to‐use, inexpensive, and relatively powerful. IoT Makers, technological do‐it‐yourself enthusiasts, utilize these technologies and the IoT ecosystem to create IoT devices that range from casual hobbyist to entrepreneurial in nature.In an effort to develop a “no‐engineer‐needed” Internet of Geophysical Things (IoGT) device, we integrated a COTS embedded computer with a geoscience‐related COTS data acquisition system (DAS). Using skills common to geoscientists, we integrated the Raspberry Pi System‐On‐Chip with the REF TEK (a Trimble brand) 130‐01 DAS. This Raspberry Pi enhanced REF TEK (RaPiER) provides IoGT Makers a platform for the development of geoscientific sensor node or network enhancements. IoGT Makers can use the RaPiER to tinker with IoT capabilities while simultaneously acquiring research‐quality data. Geoscientific (i.e., seismic) application areas that may benefit from RaPiER, particularly RaPiER nodal processing, include earthquake and engineering seismology.In this article, we discuss the emerging technologies that allow IoGT Makers to build their own IoGT device. We review our selection of the Raspberry Pi and REF TEK 130‐01 for IoGT device integration. We provide a RaPiER system integration guide and REF TEK interface software compiled for the Raspberry Pi ( available in the electronic supplement to this article). We present methods to expand RaPiER capabilities using the Raspberry Pi’s ecosystem. Last, we discuss our evaluation of RaPiER’s performance and its suitability for realistic field deployments. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.

PurposeThe purpose of this paper Computing is a recent emerging cloud model that affords clients limitless facilities, lowers the rate of customer storing and computation and progresses the ease of use, leading to a surge in the number of enterprises and individuals storing data in the cloud. Cloud services are used by various organizations (education, medical and commercial) to store their data. In the health-care industry, for example, patient medical data is outsourced to a cloud server. Instead of relying onmedical service providers, clients can access theirmedical data over the cloud.Design/methodology/approachThis section explains the proposed cloud-based health-care system for secure data storage and access control called hash-based ciphertext policy attribute-based encryption with signature (hCP-ABES). It provides access control with finer granularity, security, authentication and user confidentiality of medical data. It enhances ciphertext-policy attribute-based encryption (CP-ABE) with hashing, encryption and signature. The proposed architecture includes protection mechanisms to guarantee that health-care and medical information can be securely exchanged between health systems via the cloud. Figure 2 depicts the proposed work's architectural design.FindingsFor health-care-related applications, safe contact with common documents hosted on a cloud server is becoming increasingly important. However, there are numerous constraints to designing an effective and safe data access method, including cloud server performance, a high number of data users and various security requirements. This work adds hashing and signature to the classic CP-ABE technique. It protects the confidentiality of health-care data while also allowing for fine-grained access control. According to an analysis of security needs, this work fulfills the privacy and integrity of health information using federated learning.Originality/valueThe Internet of Things (IoT) technology and smart diagnostic implants have enhanced health-care systems by allowing for remote access and screening of patients’ health issues at any time and from any location. Medical IoT devices monitor patients’ health status and combine this information into medical records, which are then transferred to the cloud and viewed by health providers for decision-making. However, when it comes to information transfer, the security and secrecy of electronic health records become a major concern. This work offers effective data storage and access control for a smart healthcare system to protect confidentiality. CP-ABE ensures data confidentiality and also allows control on data access at a finer level. Furthermore, it allows owners to set up a dynamic patients health data sharing policy under the cloud layer. hCP-ABES proposed fine-grained data access, security, authentication and user privacy of medical data. This paper enhances CP-ABE with hashing, encryption and signature. The proposed method has been evaluated, and the results signify that the proposed hCP-ABES is feasible compared to other access control schemes using federated learning.

In the cloud, for achieving access control and keeping data confidential, the data owners could adopt attribute-based encryption to encrypt the stored data. Users with limited computing power are however more likely to delegate the mask of the decryption task to the cloud servers to reduce the computing cost. As a result, attribute-based encryption with delegation emerges. Still, there are caveats and questions remaining in the previous relevant works. For instance, during the delegation, the cloud servers could tamper or replace the delegated ciphertext and respond a forged computing result with malicious intent. They may also cheat the eligible users by responding them that they are ineligible for the purpose of cost saving. Furthermore, during the encryption, the access policies may not be flexible enough as well. Since policy for general circuits enables to achieve the strongest form of access control, a construction for realizing circuit ciphertext-policy attribute-based hybrid encryption with verifiable delegation has been considered in our work. In such a system, combined with verifiable computation and encrypt-then-mac mechanism, the data confidentiality, the fine-grained access control and the correctness of the delegated computing results are well guaranteed at the same time. Besides, our scheme achieves security against chosen-plaintext attacks under the k-multilinear Decisional Diffie-Hellman assumption. Moreover, an extensive simulation campaign confirms the feasibility and efficiency of the proposed solution.

The information-centric Internet of things (IC-IoT) is different from the traditional Internet of things (IoT) in that the device-to-device pattern is generalized to a device-to-network pattern. Furthermore, in an IC-IoT environment, there is a demand for protecting the security of all data generated from IC-IoT devices. A cryptography scheme named attribute-based encryption (ABE) represents a smart method of providing the fine-grained access control that can sufficiently protect data security. The most attractive advantage of ABE is its expressive access policy, which makes the access control of data flexible and manageable. However, there is a serious problem caused by such an access policy; it incurs a greater ciphertext redundancy and computational overhead. This implies that the current ABE scheme is hard to implement in the thin client devices of IC-IoT. In this paper, we propose a universalized policy-compacting method via sharing public parts of the policy. Compared with the original policy, the compacted policy applies a more compact ciphertext and requires less computation, communication, and storage cost. However, the policy-compacting problem is proved to be a non-deterministic polynomial complete (NPC) problem. Thus, a greedy algorithm is provided to obtain an approximate minimum compacted policy scale. Finally, we propose a compact ciphertext-policy attribute-based encryption (CCP-ABE) scheme with the policy-compacting method. A security proof and performance evaluation show that the proposed CCP-ABE scheme provides a comprehensive performance improvement.

Chapter 10 - Security and privacy challenges in healthcare using Internet of Things