Whilst the blessing of the cloud, which provides for adaptability and easy data sharing as well as storage, comes with some security doubts. We place a major responsibility on our backs of protecting the data provided by our users from any evil attempts to sneak in and also risks of being accidentally exposed and the ones that exist through shared infrastructure. This paper traverses this complex terrain by noting the need for targeted security solutions which are aimed towards dealing with the compounded nature of the fast-growing problem. By exploring such threats as data breaches, the possible encryption breaks and the nature of server sharing we will analyze the safety of cloud-based services. We position that the existing security methods, though very necessary, are not exclusive in responding to the ones posed by the new threats. This means we will therefore be rather flexible in our design of the data security in the cloud. By the way, we will employ innovative strategies, maybe, by replacing a word for words such as homomorphic encryption, zero-knowledge proofs, and federated learning, presenting how it holds promise for private and confidential assets. Furthermore, we investigate the exploding influence of blockchain technology, regarding the wages it might deserve in providing manipulated data authentication and creating trust. The work in this paper creates a path towards a future when database users' virtual information in the cloud is safe and certified. Through proposing a wide-ranged approach, which equates realized ideas with well-established security frameworks, we shall lead a cloud infrastructure where stability and power would prevail. Keywords â Cloud Computing, Security Issues, Security Challenges
Haochen Sun, J. Li, Change Institutions to: University of Waterloo
The recent surge in artificial intelligence (AI), characterized by the prominence of large language models (LLMs), has ushered in fundamental transformations across the globe. However, alongside these advancements, concerns surrounding the legitimacy of LLMs have grown, posing legal challenges to their extensive applications. Compounding these concerns, the parameters of LLMs are often treated as intellectual property, restricting direct investigations. In this study, we address a fundamental challenge within the realm of AI legislation: the need to establish the authenticity of outputs generated by LLMs. To tackle this issue, we present zkLLM, which stands as the inaugural specialized zero-knowledge proof tailored for LLMs to the best of our knowledge. Addressing the persistent challenge of non-arithmetic operations in deep learning, we introduce tlookup, a parallelized lookup argument designed for non-arithmetic tensor operations in deep learning, offering a solution with no asymptotic overhead. Furthermore, leveraging the foundation of tlookup, we introduce zkAttn, a specialized zero-knowledge proof crafted for the attention mechanism, carefully balancing considerations of running time, memory usage, and accuracy. Empowered by our fully parallelized CUDA implementation, zkLLM emerges as a significant stride towards achieving efficient zero-knowledge verifiable computations over LLMs. Remarkably, for LLMs boasting 13 billion parameters, our approach enables the generation of a correctness proof for the entire inference process in under 15 minutes. The resulting proof, compactly sized at less than 200 kB, is designed to uphold the privacy of the model parameters, ensuring no inadvertent information leakage.
We introduce Zero-Knowledge Location Privacy (ZKLP), enabling users to prove to third parties that they are within a specified geographical region while not disclosing their exact location. ZKLP supports varying levels of granularity, allowing for customization depending on the use case. To realize ZKLP, we introduce the first set of Zero-Knowledge Proof (ZKP) circuits that are fully compliant to the IEEE 754 standard for floating-point arithmetic. Our results demonstrate that our floating point circuits amortize efficiently, requiring only $64$ constraints per multiplication for $2^{15}$ single-precision floating-point multiplications. We utilize our floating point implementation to realize the ZKLP paradigm. In comparison to a baseline, we find that our optimized implementation has $15.9 \times$ less constraints utilizing single precision floating-point values, and $12.2 \times$ less constraints when utilizing double precision floating-point values. We demonstrate the practicability of ZKLP by building a protocol for privacy preserving peer-to-peer proximity testing - Alice can test if she is close to Bob by receiving a single message, without either party revealing any other information about their location. In such a configuration, Bob can create a proof of (non-)proximity in $0.26 s$, whereas Alice can verify her distance to about $470$ peers per second
Cryptographic primitives have finite security lifespans, yet many modern systemsâparticularly blockchain and zero-knowledge infrastructuresâare structurally resistant to change. Existing guidance on cryptographic agility assumes mutable environments and does not account for immutability, governance overhead, or proof system constraints. This work introduces a framework for evaluating cryptographic agility in deployed systems. We propose the Cryptographic Agility Score (CAS), a nine-dimensional evaluation model that characterizes how and where cryptographic dependencies are embedded, and how these constraints affect migration feasibility. Applying the framework to TLS and ZK rollup systems reveals a structural distinction: systems that anchor cryptographic primitives at negotiation layers achieve agility, while those that anchor them at execution layers face fundamental constraints. In ZK systems, these constraints are not only architectural but mathematical, arising from the algebraic structure of proof systems. This preprint presents the framework and its application across case studies. It is intended as a diagnostic and design tool for engineers building systems that must remain resilient under cryptographic change.
Chaehyeon Lee, Jonathan Heiss, Stefan Tai, James WonâKi Hong
Verifiable decentralized federated learning (FL) systems combining blockchains and zero-knowledge proofs (ZKP) make the computational integrity of local learning and global aggregation verifiable across workers. However, they are not end-to-end: data can still be corrupted prior to the learning. In this paper, we propose a verifiable decentralized FL system for end-to-end integrity and authenticity of data and computation extending verifiability to the data source. Addressing an inherent conflict of confidentiality and transparency, we introduce a two-step proving and verification (2PV) method that we apply to central system procedures: a registration workflow that enables non-disclosing verification of device certificates and a learning workflow that extends existing blockchain and ZKP-based FL systems through non-disclosing data authenticity proofs. Our evaluation on a prototypical implementation demonstrates the technical feasibility with only marginal overheads to state-of-the-art solutions.
With the increasingly widespread application of machine learning, how to strike a balance between protecting the privacy of data and algorithm parameters and ensuring the verifiability of machine learning has always been a challenge. This study explores the intersection of reinforcement learning and data privacy, specifically addressing the Multi-Armed Bandit (MAB) problem with the Upper Confidence Bound (UCB) algorithm. We introduce zkUCB, an innovative algorithm that employs the Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARKs) to enhance UCB. zkUCB is carefully designed to safeguard the confidentiality of training data and algorithmic parameters, ensuring transparent UCB decision-making. Experiments highlight zkUCB's superior performance, attributing its enhanced reward to judicious quantization bit usage that reduces information entropy in the decision-making process. zkUCB's proof size and verification time scale linearly with the execution steps of zkUCB. This showcases zkUCB's adept balance between data security and operational efficiency. This approach contributes significantly to the ongoing discourse on reinforcing data privacy in complex decision-making processes, offering a promising solution for privacy-sensitive applications.
The rapid development of large machine learning (ML) models requires a massive amount of training data, resulting in booming demands of data sharing and trading through data markets. Traditional centralized data markets suffer from low level of security, and emerging decentralized platforms are faced with efficiency and privacy challenges. In this paper, we propose OmniLytics+, the first decentralized data market, built upon blockchain and smart contract technologies, to simultaneously achieve 1) data (resp., model) privacy for the data (resp. model) owner; 2) robustness against malicious data owners; 3) efficient data validation and aggregation. Specifically, adopting the zero-knowledge (ZK) rollup paradigm, OmniLytics+ proposes to secret share encrypted local gradients, computed from the encrypted global model, with a set of untrusted off-chain servers, who collaboratively generate a ZK proof on the validity of the gradient. In this way, the storage and processing overheads are securely offloaded from blockchain verifiers, significantly improving the privacy, efficiency, and affordability over existing rollup solutions. We implement the proposed OmniLytics+ data market as an Ethereum smart contract [41]. Extensive experiments demonstrate the effectiveness of OmniLytics+ in training large ML models in presence of malicious data owner, and the substantial advantages of OmniLytics+ in gas cost and execution time over baselines.
Boyuan Feng, Zheng Wang, Yuke Wang, Shu Yang ¡ 5 authors
Zero knowledge Neural Networks draw increasing attention for guaranteeing computation integrity and privacy of neural networks (NNs) based on zero-knowledge Succinct Non-interactive ARgument of Knowledge (zkSNARK) security scheme. However, the performance of zkSNARK NNs is far from optimal due to the million-scale circuit computation with heavy scalar-level dependency. In this paper, we propose a type-based optimizing framework for efficient zero-knowledge NN inference, namely ZENO (ZEro knowledge Neural network Optimizer). We first introduce ZENO language construct to maintain high-level semantics and the type information (e.g., privacy and tensor) for allowing more aggressive optimizations. We then propose privacy-type driven and tensor-type driven optimizations to further optimize the generated zkSNARK circuit. Finally, we design a set of NN-centric system optimizations to further accelerate zkSNARK NNs. Experimental results show that ZENO achieves up to 8.5Ă end-to-end speedup than state-of-the-art zkSNARK NNs. We reduce proof time for VGG16 from 6 minutes to 48 seconds, which makes zkSNARK NNs practical.
Unlabelled: The convergence of artificial intelligence (AI), blockchain technology, and health care represents one of the most transformative yet technically challenging frontiers in computational medicine. As health care systems adopt data-driven paradigms for precision medicine and clinical decision support, the need for secure, privacy-preserving, and collaborative learning frameworks has become critical. This tutorial introduces a comprehensive, clinically oriented, and compliance-aware framework integrating federated learning (FL) and blockchain for secure and privacy-preserving health care analytics. FL enables collaborative training across distributed institutions without raw data sharing, in alignment with privacy regulations such as the Health Insurance Portability and Accountability Act (HIPAA) and the General Data Protection Regulation (GDPR). However, FL remains vulnerable to model poisoning and gradient leakage. To address these risks, we introduce blockchain-based FL (BCFL), which leverages blockchain's immutable ledger and decentralized consensus to enhance trust, verifiability, and auditability. The tutorial's main contributions include (1) a taxonomy of diverse medical data types and their FL requirements; (2) three integration architectures (fully coupled, semicoupled, and loosely coupled) analyzed for security, scalability, and regulatory compliance; (3) a security analysis of health care-specific vulnerabilities and mitigation strategies using advanced cryptography, such as zero-knowledge proofs, homomorphic encryption, and differential privacy; and (4) a regulatory compliance framework addressing HIPAA, GDPR, and United States Food and Drug Administration guidelines for AI-enabled medical devices. We demonstrate BCFL's relevance across major health care applications, including disease prediction, medical imaging, patient monitoring, and drug discovery, and highlight emerging research directions such as quantum-resilient cryptography, scalable interoperability, and automated compliance. This tutorial serves as a foundational resource for advancing secure, compliant, and collaborative AI in health care; fostering privacy-preserving analytics; and improving patient outcomes.
Cryptocurrency, a decentralized frame of computerized cash, has picked up ubiquity around the world. In any case, its broad selection has brought consideration to noteworthy security concerns. This paper presents a exhaustive examination of cryptocurrency security, distinguishing key challenges and proposing arrangements to support the security of advanced assets. The paper starts by talking about the foundational innovation of cryptocurrencies, specifically blockchain, which offers straightforwardness and unchanging nature but is defenceless to assaults such as 51% assaults and double-spending. It at that point digs into security dangers related with cryptocurrency capacity and trade stages, counting wallet vulnerabilities and hacking incidents. Current security best hones, such as multi-signature wallets and cold storage solutions, are analysed, alongside rising innovations like zero-knowledge proofs and homomorphic encryption. Furthermore, the part of administrative systems in advancing cryptocurrency security is investigated, highlighting the require for a adjusted approach that energizes advancement whereas securing investors. In conclusion, guaranteeing the security of cryptocurrencies is basic for cultivating believe and widespread adoption. By tending to vulnerabilities and executing vigorous security measures, partners can relieve dangers and open the complete potential of advanced monetary forms. This paper contributes profitable experiences to the continuous discourse on cryptocurrency security and recommends roads for future inquire about in this energetic field.
In the rapidly evolving domain of the Internet of Things (IoT) and fog computing, maintaining security, scalability, and efficient operation poses significant challenges. Addressing these issues, this study introduces "BlockFog," a novel blockchain-based framework designed to bolster intrusion defense in IoT fog computing environments. The core objective of BlockFog is to counteract the vulnerabilities inherent in decentralized IoT ecosystems by leveraging blockchain technology for enhanced security and transparency. The framework's innovative design integrates crucial components such as Device Onboarding & Identity Management, Data Integrity & Logging, Smart Contract-Driven Intrusion Detection, Automated Blockchain Responses, Secure Peer-to-Peer Communication, and a Lightweight Consensus Mechanism. These elements work collectively to ensure the security and functionality of IoT devices within the fog computing paradigm. BlockFog stands out for its meticulous approach to handling high transaction volumes with off-chain computations and layer-2 solutions, ensuring data integrity and facilitating seamless audit processes. The framework's resilience is further demonstrated through its robust response to evolving cyber threats, incorporating Over-the-Air (OTA) updates and advanced data protection mechanisms like zero-knowledge proofs. A comparative analysis highlights BlockFog's superior performance against existing models. The results reveal BlockFog's lower latency rates in normal, high traffic, and attack scenarios, its higher throughput efficiency, and its more effective resource utilization in terms of CPU, memory, and bandwidth usage. Moreover, BlockFog exhibits an enhanced ability to detect and respond to malicious activities, including DDoS attacks, with significantly higher accuracy than its counterparts. These findings underscore BlockFog's potential in redefining security and operational paradigms in IoT fog computing, making it a robust, agile, and transparent framework suitable for the current digital landscape.
Zero-knowledge proofs (ZKPs) are computationally demanding to generate. Their importance for applications like ZK-Rollups has prompted some to outsource ZKP generation to a market of specialized provers. However, existing market designs either do not fit the ZKP setting or lack formal description and analysis. In this work, we propose a formal ZKP market model that captures the interactions between users submitting ZKP tasks and provers competing to generate proofs. Building on this model, we introduce $Proo\varphi$, an auction-based ZKP market mechanism. We prove that $Proo\varphi$ is incentive compatible for users and provers, and budget balanced. We augment $Proo\varphi$ with system-level designs to address the practical challenges of our setting, such as Sybil attacks, misreporting of prover capacity, and collusion. We analyze our system-level designs and show how they can mitigate the various security concerns.
Zero-knowledge proofs (ZKPs) are computationally demanding to generate. Their importance for applications like ZK-Rollups has prompted some to outsource ZKP generation to a market of specialized provers. However, existing market designs either do not fit the ZKP setting or lack formal description and analysis. In this work, we propose a formal ZKP market model that captures the interactions between users submitting ZKP tasks and provers competing to generate proofs. Building on this model, we introduce $ProoĎ$, an auction-based ZKP market mechanism. We prove that $ProoĎ$ is incentive compatible for users and provers, and budget balanced. We augment $ProoĎ$ with system-level designs to address the practical challenges of our setting, such as Sybil attacks, misreporting of prover capacity, and collusion. We analyze our system-level designs and show how they can mitigate the various security concerns.
Usama Arshad, Zahid Halim, Hisham Alasmary, Muhammad Waqas
Blockchain technology is used often as a merger with other technologies to achieve a high level of security, privacy, and robustness and to handle issues such as maliciousness of nodes, privacy leakage, the selfishness of nodes, communication delays, and high execution and transaction costs. There is currently a lack of a comprehensive system for automating and cost-effectively managing vehicle repairs, maintenance, and other associated services. To solve such issues we proposed a novel futuristic comprehensive model that integrates a blockchain-based framework to safely record vehicle maintenance, validate repair services, and oversee parts inventory. It employs smart contracts and consensus protocols to secure communications and data storage, thus reducing data breaches and vulnerabilities from single-point failures. A reward system is embedded within the network to encourage positive behavior and deter detrimental actions. We also incorporated advanced privacy-ensuring methods, like zero-knowledge proofs and secure multi-party computation, to safeguard sensitive data while preserving its utility. Our model features automatic detection and response mechanisms for node failure, improving network resilience by 25% thus also providing a 20% reduction in execution, operational costs, and scalability with an enhancement of 15%, underscoring the modelâs efficiency in vehicular repair and maintenance activities. Results and simulations clearly depict the overall performance and efficiency in terms of security, privacy, node failure, and the management of vehicle repairs with respect to other closely related models.
In a multiparty signing protocol, also known as a threshold signature scheme, the private signing key is shared amongst a set of parties and only a quorum of those parties can generate a signature. Research on multiparty signing has been growing in popularity recently due to its application to cryptocurrencies. Most work has focused on reducing the number of rounds to two, and as a result: (a) are not fully simulatable in the sense of MPC real/ideal security definitions, and/or (b) are not secure under concurrent composition, and/or (c) utilize non-standard assumptions of different types in their proofs of security. In this paper, we describe a simple three-round multiparty protocol for Schnorr signatures that is secure for any number of corrupted parties; i.e., in the setting of a dishonest majority. The protocol is fully simulatable, secure under concurrent composition, and proven secure in the standard model or random-oracle model (depending on the instantiations of the commitment and zero-knowledge primitives). The protocol realizes an ideal Schnorr signing functionality with perfect security in the ideal commitment and zero-knowledge hybrid model (and thus the only assumptions needed are for realizing these functionalities). In our presentation, we do not assume that all parties begin with the message to be signed, the identities of the participating parties and a unique common session identifier, since this is often not the case in practice. Rather, the parties achieve consensus on these parameters as the protocol progresses.
Biscuit is a recent multivariate signature scheme based on the MPC-in-the-Head paradigm. It has been submitted to the NIST competition for additional signature schemes. Signatures are derived from a zero-knowledge proof of knowledge of the solution of a structured polynomial system. This extra structure enables efficient proofs and compact signatures. This short note demonstrates that it also makes these polynomial systems easier to solve than random ones. As a consequence, the original parameters of Biscuit failed to meet the required security levels and had to be upgraded.
Verifiable encryption (VE) is a protocol where one can provide assurance that an encrypted plaintext satisfies certain properties, or relations. It is an important building block in cryptography with many useful applications, such as key escrow, group signatures, optimistic fair exchange, and others. However, the majority of previous VE schemes are restricted to instantiation with specific public-key encryption schemes or relations. In this work, we propose a novel framework that realizes VE protocols using zero-knowledge proof systems based on the MPC-in-the-head paradigm (Ishai et al. STOC 2007). Our generic compiler can turn a large class of zero-knowledge proofs into secure VE protocols for any secure public-key encryption scheme with the undeniability property, a notion that essentially guarantees binding of encryption when used as a commitment scheme. Our framework is versatile: because the circuit proven by the MPC-in-the-head prover is decoupled from a complex encryption function, the work of the prover is focused on proving the encrypted data satisfies the relation, not the proof of plaintext knowledge. Hence, our approach allows for instantiation with various combinations of properties about the encrypted data and encryption functions. We then consider concrete applications, to demonstrate the efficiency of our framework, by first giving a new approach and implementation to verifiably encrypt discrete logarithms in any prime order group more efficiently than was previously known. Then we give the first practical verifiable encryption scheme for AES keys with post-quantum security, along with an implementation and benchmarks.
The promise of combining blockchain with artificial intelligence (AI) is compelling: auditable data provenance for training sets, tamper-evident logging for model lifecycle events, decentralized marketplaces for models and datasets, and automated enforcement of usage policies via smart contracts. Yet organizations quickly discover that operationalizing blockchain-based AI goes beyond stitching together two popular technologies. Differences in trust assumptions, latency and throughput profiles, security primitives, compliance expectations, and tooling maturity frequently collide at deployment time. This manuscript organizes those frictions into a coherent integration problem space and proposes a reference architecture and evaluation methodology to reason about trade-offs. We review the literature on blockchain consensus and scalability, privacy-preserving machine learning (federated learning, differential privacy, secure computation, and zero-knowledge proofs), data governance and compliance (e.g., GDPR), and MLOps platforms. We then present a methodology that stress-tests seven integration dimensions: architecture and partitioning (on-chain vs. off-chain responsibilities), performance and cost (latency, throughput, gas), privacy and confidentiality (leakage risks and mitigations), security and integrity (tamper-evidence, oracle trust), interoperability (heterogeneous chains and toolchains), compliance and governance (auditability versus erasure rights), and human/organizational fit (DevOps, incident response, and skills).
Suppakit Waiwitlikhit, Ion Stoica, Yi Sun, Tatsunori Hashimoto ¡ 5 authors
There is an increasing conflict between business incentives to hide models and data as trade secrets, and the societal need for algorithmic transparency. For example, a rightsholder wishing to know whether their copyrighted works have been used during training must convince the model provider to allow a third party to audit the model and data. Finding a mutually agreeable third party is difficult, and the associated costs often make this approach impractical. In this work, we show that it is possible to simultaneously allow model providers to keep their model weights (but not architecture) and data secret while allowing other parties to trustlessly audit model and data properties. We do this by designing a protocol called ZkAudit in which model providers publish cryptographic commitments of datasets and model weights, alongside a zero-knowledge proof (ZKP) certifying that published commitments are derived from training the model. Model providers can then respond to audit requests by privately computing any function F of the dataset (or model) and releasing the output of F alongside another ZKP certifying the correct execution of F. To enable ZkAudit, we develop new methods of computing ZKPs for SGD on modern neural nets for simple recommender systems and image classification models capable of high accuracies on ImageNet. Empirically, we show it is possible to provide trustless audits of DNNs, including copyright, censorship, and counterfactual audits with little to no loss in accuracy.
To fundamentally solve the "prisoner's dilemma" of the incentive system, we propose an incentive model built from the existing blockchain ecology.It uses blockchain as the underlying technology, federal learning as the operational basis, a decentralized autonomous organization as the organizational form, smart contract as the means of implementation, and non-homogeneous pass-through as the incentive mechanism, and the core method is to use zero-knowledge proof in privacy computing to build a trustworthy and reliable management decision to achieve "power and responsibility matching" more efficiently.
The rapid proliferation of AI-generated âdeepfakeâ images, audio, and video is eroding public trust in digital media and amplifying risks to elections, markets, journalism, and personal safety. While AI detection models have improved, they face an adversarial âcat-and-mouseâ problem and often struggle to generalize across manipulation methods and compression regimes. This manuscript proposes and analyzes a hybrid, end-to-end approach that couples upstream provenance and authenticity signalsâanchored via open standards (e.g., C2PA Content Credentials) and decentralized ledgersâwith downstream AI detection and moderation. The pipeline captures and signs media at source; binds verifiable, tamper-evident metadata; anchors cryptographic hashes on a public or consortium blockchain; stores originals off-chain with content addressing (e.g., IPFS/Filecoin); and fuses these trust signals with model-based detectors and policy engines at distribution edges. We situate the proposal within current regulation (e.g., EU AI Act transparency duties) and state-of-the-art methods (e.g., watermarking such as SynthID, Stable Signature, and Tree-Ring; deepfake detectors trained on DFDC and FaceForensics++), highlighting both strengths and known attack vectors against watermarking that motivate layered defenses. A simulation-based evaluation illustrates that combining provenance signals with video-level transformer detectors can raise F1 from 0.85 to 0.92 while cutting false positives by ~41% in a balanced test set, primarily by rejecting credential-mismatched or hash-divergent media before expensive model inference. We further discuss privacy-preserving verification using W3C Verifiable Credentials (VC 2.0), Decentralized Identifiers (DIDs), and selective-disclosure with zero-knowledge proofs. The findings make a practical case for âtrust by designâ built on open standards, decentralized integrity proofs, and robust AI detection, implemented as a policy-aware defense-in-depth stack for platforms and newsrooms.
D Davidson, Nicola Vasey, Adam Pattison Rathbone, Charlotte Lucy Richardson
Abstract Introduction Pharmacy education in the United Kingdom must adapt to produce independent prescribing pharmacists ready to join an evolving healthcare system. Current placement practices exclude approximately 21% of the population due to a lack of specific recommendations surrounding paediatric experience and knowledge within the MPharm programme.[1] In turn, studentsâ current experiences may be limited by lack of interaction with unique learning outcomes offered by some healthcare settings, such as paediatric hospitals.[2] To promote student exposure in an overlooked speciality, novel approaches to work-based learning can be utilised. Aim This study aimed to explore pharmacy studentsâ experiences of work-based learning in a paediatric hospital setting. Methods In October 2022, fourth-year MPharm students at one school of pharmacy were invited to undertake work-based learning sessions across one academic year. The sessions aimed to develop studentsâ paediatric consultations skills and knowledge. Sessions consisted of a briefing, ward activities, scaffolded consultations with children and carers, and debriefs with a clinical supervisor. Debriefs included students reporting clinical information, required action and learning outcomes. All debriefs provided by students were transcribed by a clinical supervisor using a spreadsheet which recorded the date, ward visited, patient details, student handover, follow-up (if required) and learning outcomes. Data was initially cleaned, quality checked, and underwent content analysis to identify patterns and key themes to describe student experiences. Results Seventy-four students took part in sessions and delivered 233 consultations covering the medical history of the patient (76%, n=177), with varied levels of completeness. Students were exposed to acute conditions (41%, n=96) and chronic conditions (33%, n=76), with 13% (n=30) still awaiting diagnosis. Forty-eight percent (n=81) of learning points related to the pathology, diagnosis and symptoms of conditions, 24% (n=41) to medicines, 15% (n=25) to patient care, 11% (n=18) to non-clinical experiences and 2% (n=4) to other outcomes. In addition to carrying out ward activities, students underwent the processing of experiences during post-session debriefs: âItâs uncomfortable seeing a child struggle to breatheâ [P131]. The process of active reflection was also evidenced in debriefs: âI felt very anxious, like a tightness in the chest, to hear that a child had a short life expectancyâ [P145]; âI realised they had zero cultural competence after seeing a patient from the Middle East with jaundiceâ [P233]. Conclusion The study demonstrates a proof of concept that students can be exposed to complex care needs and challenging consultations under indirect supervision, demonstrating the paediatric setting to be a suitable work-based learning host. However, findings are limited to a single cohort of students at a single site, meaning transferability may be limited. Future studies could focus on longitudinal educational and emotional outcomes of students by measuring clinical knowledge, competence and confidence. Utilising post-session debriefs with peers and supervisors created a space to share both pharmaceutical and emotional learning points, aiding in managing the cognitive load of students. This experience not only highlights the requirement of paediatric exposure in pharmacy education programmes to aid the studentsâ future practice, but the importance of supervised reflective activities following work-based learning experiences. References 1. Office for National Statistics. Ethnic group by age and sex, England and Wales: Census 2021. 2023. Available from: https://www.ons.gov.uk/peoplepopulationandcommunity/culturalidentity/ethnicity/articles/ethnicgroupbyageandsexenglandandwales/census2021 2. Kerth J-L, van Treel L, Bosse HM. The Use of Entrustable Professional Activities in Pediatric Postgraduate Medical Education: A Systematic Review. Academic Pediatrics. 2022;22(1):21-8.
Abstract As an important manifestation of the current development and transformation of the worldâs power and energy industries, the virtual power plant is an important foundation for optimizing the layout of energy resources. However, since there are many open channels in the virtual power plant, adversaries can implement eavesdropping, replay, impersonation, forgery, and other attacks to access the virtual power plant, and even publish false data in the virtual power plant to disrupt the operation of the virtual power plant. In addition, it is easy for an adversary to deduce key information such as the layout of virtual power plant equipment through the identity of the device. In this context, to ensure the security and privacy of devices when accessing the platform, in this paper, we propose an efficient authentication protocol based on the elliptic curve cryptography and zero-knowledge proof, which requires only two information exchanges. Security analysis shows that the proposed protocol can meet security features such as mutual authentication, key agreement, perfect forward secrecy, and device anonymity. Performance analysis indicates that the proposed protocol achieves a reasonable balance between computational and signaling overhead, and it is more suitable for achieving efficient device authentication and privacy protection in virtual power plants.
This article delves into the inherent security of blockchain technology by evaluating the sophisticated techniques it employs. Key among these are mathematical hash functions, elliptic curve cryptography, and zero-knowledge proofs. Mathematical hash functions ensure that data stored is immutable; any slight alteration to the information will lead to a drastically different hash output, making any tampering evident. Elliptic curve cryptography provides a robust encryption mechanism, ensuring that data transactions remain confidential and secure. Meanwhile, zero-knowledge proofs enable one party to prove to another that they possess specific knowledge without revealing the actual information, further bolstering privacy. Owing to these technological underpinnings, blockchain not only excels in safeguarding sensitive data but also facilitates operations like verifying information authenticity. Moreover, in sectors like supply chain management, it offers capabilities for precise logistics positioning and traceability. Such applications underline blockchainâs potential as a tool for transparency and security in various industries. Through these features and mechanisms, blockchain stands as an exemplar of digital security in todayâs interconnected era.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques