AI has already begun to lead transformation across industries, along with AI APIs such as predictive modeling and Natural Language Processing. Just like them, AI applications are on the rise as well. Unfortunately, those typically operate under the hackneyed idea of having a universal centralized access control entity, which are not only prone to security breaches, rather they lack efficiency and access transparency as well. Consequently, this hinders attempts made to secure sensitive data and enforce accountability in AI systems. This paper proposes a new system model based on blockchain technology and smart contracts and discusses how it manages access control to AI APIs. This solution tackles the limitations of conventional systems such as security, scalability, and transparency brought by decomposing access rights. There are different possibilities for monitoring and enforcing security policies, One of them, Dynamic access control prevents unauthorized users from gaining access to any resource. In addition, blockchain guarantees auditing by virtue of its decentralized nature, eliminating reliance on central entities. The proposed approach is substantiated through a combination of simulations alongside proof-of-concept implementations, demonstrating the ability to decrease access latency, enhance resistance to rogue access attempts and access management processes. This work increasingly enhances the decentralized security solutions available by providing an effective solution for access control in the context of centralized AI API’s for both enterprise and government use cases being scalable and robust as well as transparent.
With the rise of machine learning techniques, ensuring the fairness of decisions made by machine learning algorithms has become of great importance in critical applications. However, measuring fairness often requires full access to the model parameters, which compromises the confidentiality of the models. In this paper, we propose a solution using zero-knowledge proofs, which allows the model owner to convince the public that a machine learning model is fair while preserving the secrecy of the model. To circumvent the efficiency barrier of naively proving machine learning inferences in zero-knowledge, our key innovation is a new approach to measure fairness only with model parameters and some aggregated information of the input, but not on any specific dataset. To achieve this goal, we derive new bounds for the fairness of logistic regression and deep neural network models that are tighter and better reflecting the fairness compared to prior work. Moreover, we develop efficient zero-knowledge proof protocols for common computations involved in measuring fairness, including the spectral norm of matrices, maximum, absolute value, and fixed-point arithmetic. We have fully implemented our system, FairZK, that proves machine learning fairness in zero-knowledge. Experimental results show that FairZK is significantly faster than the naive approach and an existing scheme that use zero-knowledge inferences as a subroutine. The prover time is improved by 3.1x--1789x depending on the size of the model and the dataset. FairZK can scale to a large model with 47 million parameters for the first time, and generates a proof for its fairness in 343 seconds. This is estimated to be 4 orders of magnitude faster than existing schemes, which only scale to small models with hundreds to thousands of parameters.
Blockchain technology represents a secure, decentralized solution for transaction and data management. Nonetheless, large-scale blockchain networks continue to face issues regarding security, scalability, and reliability — especially as the volume of transactions grows and cyber threats advance. This study addresses the improvement of security and reliability for blockchain networks using AI- optimized consensus protocols embedded in smart contracts. Smart contracts are self-executing agreements that are written into the code of blockchain networks to automate the process of a transaction, but like all computer programs, they are limited by their deterministic nature and are vulnerable to coding errors and exploits. Using artificial intelligence (AI), more specifically, machine learning (ML) techniques, this study seeks to overcome these shortcomings, and proposes a better protected, more flexible and resilient transaction perimeter. AI consensus algorithms build on existing consensus paradigms like Proof of Work (PoW) and Proof of Stake (PoS), and optimize the decision-making process while providing capabilities against malicious attacks and ensuring real-time efficiency in the verification process. This paper examines how artificial intelligence can contribute to the dynamic enhancement of consensus algorithms, allowing the blockchain environment to better identify anomalies, mitigate attacks, and optimizing resource allocation. In addition, AI-based smart contracts can learn from historical data and dynamically configure their execution parameters to reduce vulnerabilities and improve robustness. AI and blockchain together encourage a safer environment where transactions are more reliable, and networks are more resilient to external threats. These results reveal how the combination of artificial intelligence-backed consensus methods and automated smart contracts enhance transaction integrity, security, and efficiency of blockchain frameworks, potentially leading toward safer, scalable, reliable decentralized networks.
Efficient contract management is essential for ensuring sustainable and reliable supply chains; yet, traditional methods remain manual, error-prone, and inefficient, leading to delays, financial risks, and compliance challenges. AI and blockchain technology offer a transformative alternative, enabling the establishment of automated, transparent, and self-executing smart contracts that enhance efficiency and sustainability. As part of AI-driven smart contract automation, we previously implemented contractual clause extraction using question answering (QA) and named entity recognition (NER). This paper presents the next step in the information extraction process, relation extraction (RE), which aims to identify relationships between key legal entities and convert them into structured business rules for smart contract execution. To address RE in legal contracts, we present a novel hierarchical transformer model that captures sentence- and document-level dependencies. It incorporates global and segment-based attention mechanisms to extract complex legal relationships spanning multiple sentences. Given the scarcity of publicly available contractual datasets, we also introduce the contractual relation extraction (ContRE) dataset, specifically curated to support relation extraction tasks in legal contracts, that we use to evaluate the proposed model. Together, these contributions enable the structured automation of legal rules from unstructured contract text, advancing the development of AI-powered smart contracts.
We develop and apply epistemic tests to various decentralized governance methods as well as to study the impact of participation. These tests probe the ability to reach a correct outcome when there is one. We find that partial abstention is a strong governance method from an epistemic standpoint compared to alternatives such as various forms of ``transfer delegation" in which voters explicitly transfer some or all of their voting rights to others. We make a stronger case for multi-step transfer delegation than is present in previous work but also demonstrate that transfer delegation has inherent epistemic weaknesses. We show that enhanced direct participation, voters exercising their own voting rights, can have a variety of epistemic impacts, some very negative. We identify governance conditions under which additional direct participation is guaranteed to do no epistemic harm and is likely to increase the probability of making correct decisions. In light of the epistemic challenges of voting-based decentralized governance, we consider the possible supplementary use of prediction markets, auctions, and AI agents to improve outcomes. All these results are significant because epistemic performance matters if entities such as DAOs (decentralized autonomous organizations) wish to compete with organizations that are more centralized.
Alex Wong, Duncan McFarlane, Charlotte Ellarby, M.B. Lee · 5 authors
Twenty-five years ago, the specification of the Intelligent Product was established, envisaging real-time connectivity that not only enables products to gather accurate data about themselves but also allows them to assess and influence their own destiny. Early work by the Auto-ID project focused on creating a single, open-standard repository for storing and retrieving product information, laying a foundation for scalable connectivity. A decade later, the approach was revisited in light of low-cost RFID systems that promised a low-cost link between physical goods and networked information environments. Since then, advances in blockchain, Web3, and artificial intelligence have introduced unprecedented levels of resilience, consensus, and autonomy. By leveraging decentralised identity, blockchain-based product information and history, and intelligent AI-to-AI collaboration, this paper examines these developments and outlines a new specification for the Intelligent Product 3.0, illustrating how decentralised and AI-driven capabilities facilitate seamless interaction between physical AI and everyday products.
Integrating AI and blockchain technologies holds significant potential for enhancing e-governance, particularly in improving predictive policy execution within smart cities. This study conducts a comprehensive review and bibliometric analysis of existing literature to identify trends, key publications, and research gaps. Using peer-reviewed articles indexed by Scopus and published between 2019 and 2024, we observe a significant rise in research output, focusing on the separate applications of AI and blockchain in e-governance. Key themes identified include enhanced transparency, efficiency in public services, and concerns related to data privacy. However, our analysis uncovers a clear gap in empirical studies addressing the combined use of AI and blockchain technologies. The bibliometric coupling map reveals central clusters around “smart city” and “blockchain,” while topics such as “sustainability” and “climate change” show significant impact, highlighting their relevance to governance. Additionally, the study identifies a lack of cross-disciplinary research, emphasizing the need for future interdisciplinary collaborations. Despite the insights gained, the study is constrained by its reliance on bibliometric methods, which may not capture the complexities of real-world technology integration. Future research should prioritize longitudinal case studies and pilot projects to address regulatory, ethical, and practical challenges, contributing to the responsible adoption of AI and blockchain in digital governance.
Implications of blockchain-integrated health data management in metaverse environments in India, where digital transformation is happening at a rapid pace and a unique regulatory and socio-cultural landscape exists, require special focus. This research explores the subtleties of privacy, ownership ownership , and consent in blockchain-enabled health data frameworks in the context of emerging India and addresses the operational and ethical issues of finding a balance between decentralized data structures, India’s regulatory expectations, and the multitude of needs of its population, providing a holistic view of policy and legal considerations for this domain. In India, the IT Act, 2000, and the recently enacted DPDPA 2023 are the laws that exist to deal with privacy concerns, but such laws were built for centralized applications and do not apply to decentralized and immutable blockchain structures. This paper explores how blockchain’s features, including self-sovereign identity and zero-knowledge proofs, can be leveraged toward align with Indian privacy norms, potentially allowing users to retain ownership of their health data while adhering to legal obligations. Data ownership in traditional healthcare systems is ambiguous; who owns the data varies from patient to provider, from provider to provider, and from provider to digital platform. Blockchain technology offers the opportunity for self-sovereign ownership. This paper studies the consequences of decentralized ownership by analyzing how the Indian legal framework may need to be reformed to reconcile user autonomy with public health interests in a decentralized ownership environment. It examines these risks, discusses ethical considerations, and explores policy approaches that could be taken to ensure informed consent informed consent is truly respected and risks are addressed in the virtual health space.
With the rapid expansion of blockchain applications, smart contracts are becoming increasingly complex, making the automated detection of contract vulnerabilities more critical than ever. Large language models, due to their advanced code comprehensive ability, are considered to have the potential to undertake the task of automated software vulnerability discovery. Although there have been empirical studies on ChatGPT's automated discovery of contract vulnerabilities, the current empirical research has not addressed how well ChatGPT can detect logical vulnerabilities in smart contracts or whether ChatGPT's detection performance for logical vulnerabilities can be improved. To fill this gap, this study collected and organized seven types of logical vulnerability source codes from 6165 real smart contract audit reports and three datasets, such as Web3Bugs, and used this database to validate ChatGPT's detection capability for logical vulnerabilities. To improve ChatGPT's accuracy in detecting logical vulnerabilities, we fine-tuned ChatGPT with a dataset marked with a specific method, achieving an average accuracy rate of 95% for single vulnerability detection per training session. We improved the original marking method to increase further the number of vulnerabilities that a single model can detect. We used a specific completion marking format, ultimately enabling ChatGPT to detect various logical vulnerabilities. In terms of enhancing model scalability, we found a special training set marking method that allows for the addition of detectable vulnerability types through secondary training.
Primavera De Filippi, Morshed Mannan, Wessel Reijers
ABSTRACT Emerging technologies pose many new challenges for regulation and governance on a global scale. With the advent of distributed communication networks like the Internet and decentralized ledger technologies like blockchain, new platforms emerged, disrupting existing power dynamics and bringing about new claims of sovereignty from the private sector. This special issue addresses a gap in the literature by focusing the discourse on the issue of trust and confidence in the digital realm. In particular, looking at the evolution of the web (from Web 1.0, to Web 2.0, and then Web 3), this article analyses how every iteration reflects a different way of dealing with the problem of trust online, resulting in a different regulation and governance landscape. Technology is often regarded as a new lever of regulation, attempting to resolve the problem of “trust” online, either through the introduction of a new trusted authority (Web 2.0) or through the introduction of technological guarantees that provide more assurance—or “confidence”—in the way interactions can be operationalized (Web 3). Yet, each of these technologies also introduce new risks and governance costs, ultimately shifting the problem of trust in a new direction rather than resolving it or removing the need for trust altogether. The main contribution of the articles in this special issue is providing a better understanding of the trust challenges faced and posed by emerging technologies and demonstrating how they affect institutional governance—in both theory and practice—with a view to help policymakers find appropriate answers to these challenges.
V. Rama Krishna, Abdullah H Maad, Ali Ihsan Alanssari, Nour Rahim Nimah · 6 authors
This article discusses how integrating AI and blockchain technology into digital health platforms might help and hurt privacy, fairness, transparency, and compliance. This research compares AI and blockchain technologies for making honest and ethical healthcare choices. We are investigating federated learning, homomorphic encryption, differential privacy, zero-knowledge proofs, self-sovereign identity systems, explainable AI, blockchain interface protocols, and privacypreserving AI systems. We rated each technique based on data protection, ethical data collecting, computer justice, openness, and system security. While most approaches perform well in certain locations, they all have issues that may render them unsuitable for use in healthcare. The proposed solution addresses these concerns and outperforms speed standards. It evaluates ethical risks based on bias, fairness, and transparency and is continuously improving ethical decision-making. These evaluations improve healthcare AI systems' reliability, fairness, and clarity during decision-making. This implies its potential application in AI-driven healthcare systems.
Blockchain Technology Applications and Security
Ethics and Social Impacts of AI
Neuroethics, Human Enhancement, Biomedical Innovations
Nishant Jagannath, Christopher Kevin Wong, Braden Mcgrath, Md. Faruque Hossain · 7 authors
The rapid advancement of artificial intelligence (AI) has brought about sophisticated models capable of various tasks ranging from image recognition to natural language processing. As these models continue to grow in complexity, ensuring their trustworthiness and transparency becomes critical, particularly in decentralized environments where traditional trust mechanisms are absent. This paper addresses the challenge of verifying personalized AI models in such environments, focusing on their integrity and privacy. We propose a novel framework that integrates zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARKs) with Chainlink decentralized oracles to verify AI model performance claims on blockchain platforms. Our key contribution lies in integrating zk-SNARKs with Chainlink oracles to securely fetch and verify external data to enable trustless verification of AI models on a blockchain. Our approach addresses the limitations of using unverified external data for AI verification on the blockchain while preserving sensitive information of AI models and enhancing transparency. We demonstrate our methodology with a linear regression model predicting Bitcoin prices using on-chain data verified on the Sepolia testnet. Our results indicate the framework's efficacy, with key metrics including proof generation taking an average of 233.63 seconds and verification time of 61.50 seconds. This research paves the way for transparent and trustless verification processes in blockchain-enabled AI ecosystems, addressing key challenges such as model integrity and model privacy protection. The proposed framework, while exemplified with linear regression, is designed for broader applicability across more complex AI models, setting the stage for future advancements in transparent AI verification.
A blockchain is a trustless system in an environment populated by untrusted peers. Code deployed in blockchain as a smart contract should be cautious when invoking contracts of other peers as they might introduce several risks and unexpected issues. This paper presents an information flow-based approach for detecting cross-contract invocations to untrusted contracts, written in general-purpose languages, that could lead to arbitrary code executions and store any results coming from them. The analysis is implemented in GoLiSA, a static analyzer for Go. Our experimental results show that GoLiSA is able to detect all vulnerabilities related to untrusted cross-contract invocations on a significant benchmark suite of smart contracts written in Go for Hyperledger Fabric, an enterprise framework for blockchain solutions.
As with the increasing usage of blockchain especially in decentralized systems, the need for solid security measures increases to stop vulnerabilities in smart contracts. Our method is an ML-based solution to automate the detection of smart contract vulnerabilities and improves the blockchain security. In this paper, we propose a comprehensive vulnerability detection algorithm that employs a hybrid detection mechanism that analyzes transaction patterns and uses ML models to detect suspicious activities on the fly. The algorithm detects as well as flags smart contract vulnerabilities and malicious transactions by analyzing transaction amounts, user behavior, and frequency. The developed framework not only lessens manual effort but also automates validation so as to improve mitigation process in case of unforeseen risks. This essentially shows the capability of using ML-driven analysis to secure the blockchain environment as an adaptable approach for the detection of smart contract vulnerabilities.
The purpose of this chapter is to examine the ethical concerns and benefits associated with the integration of Artificial Intelligence (AI), particularly chatbots, in education. A comprehensive literature review was conducted to identify and analyze ethical considerations related to AI in education. The applicability of the web3 application in the identification and analysis of ethical concerns of AI chatbots is also explored. The findings revealed a spectrum of ethical concerns spanning fairness, transparency, privacy, autonomy, and educational inequality. Ethical concerns also include the misuse of private data, algorithmic biases, surveillance, and threats to job security. Conversely, the benefits of AI in education encompass improved learner experiences, enhanced teaching efficiency, and personalized learning opportunities. Chatbots, in particular, demonstrate potential in fostering engagement, increasing interest in subjects, and offering immediate support to students.
AI in Service Interactions
Ethics and Social Impacts of AI
Artificial Intelligence in Healthcare and Education
Personal AI assistants (e.g., Apple Intelligence, Meta AI) offer proactive recommendations that simplify everyday tasks, but their reliance on sensitive user data raises concerns about privacy and trust. To address these challenges, we introduce the Guardian of Data (GOD), a secure, privacy-preserving framework for training and evaluating AI assistants directly on-device. Unlike traditional benchmarks, the GOD model measures how well assistants can anticipate user needs-such as suggesting gifts-while protecting user data and autonomy. Functioning like an AI school, it addresses the cold start problem by simulating user queries and employing a curriculum-based approach to refine the performance of each assistant. Running within a Trusted Execution Environment (TEE), it safeguards user data while applying reinforcement and imitation learning to refine AI recommendations. A token-based incentive system encourages users to share data securely, creating a data flywheel that drives continuous improvement. Specifically, users mine with their data, and the mining rate is determined by GOD's evaluation of how well their AI assistant understands them across categories such as shopping, social interactions, productivity, trading, and Web3. By integrating privacy, personalization, and trust, the GOD model provides a scalable, responsible path for advancing personal AI assistants. For community collaboration, part of the framework is open-sourced at https://github.com/PIN-AI/God-Model.
Owing to the swift advancement of technology and the unfamiliarity of the execution environment, the development of Solidity smart contracts from scratch often results in significant vulnerabilities.In contrast, automated code generation enhances productivity, minimizes development time, and enables developers to focus on high-level tasks and fundamental logic.In consideration of these two viewpoints, this paper examines the utilization of large language models (LLMs) for the automatic generation of Solidity smart contracts based on specified criteria, while simultaneously ensuring the elimination of vulnerabilities through a novel masking strategy.To achieve this, we propose SolGen, a framework for generating secure Solidity smart contract code using LLMs.We assess the performance of existing LLMs (i.e.ChatGPT and Meta AI) for secure Solidity code generation.Our research indicates that ChatGPT outperforms Meta AI in performance, yielding a greater percentage of syntactically accurate and secure code.Additionally, we examine the impact of temperature adjustment on the security of generated contracts using an open-source LLM, Llama3.Our findings suggest that a temperature setting of 0.7 is optimal for the generation of Solidity code, considerably exceeding the performance of both lower and higher settings (0.1 and 1.2), especially with regard to the compilability of the code.
This article explores the transformative potential of blockchain technology in maintaining test data integrity across regulated industries, particularly in finance, healthcare, and pharmaceuticals. The article examines how blockchain's inherent characteristics address critical challenges in data security, compliance, and operational efficiency. Through analysis of implementation strategies, industry-specific applications, and emerging trends, the article demonstrates how blockchain technology revolutionizes test data management through immutable record-keeping, decentralized architecture, and smart contract automation. The article indicates significant improvements in data security, compliance management, and operational efficiency across all examined sectors, while highlighting the importance of structured implementation approaches and industry-specific considerations.
Abstract Decentralized Autonomous Organizations (DAOs) are attracting interest from various disciplines, particularly business and economics, and computer science. However, much like the parable of the blind men and the elephant, where each observer sees only part of the phenomenon, DAO research has largely remained fragmented across disciplines, limiting a comprehensive understanding of the potential of DAOs. This paper investigates to which extent DAO scholarship has achieved meaningful interdisciplinary integration. We address this question through an analysis of knowledge flows between Business and Economics and Computer Science, using citation network analysis, topic modelling, and outlet analysis. We find that while DAOs generate vibrant interdisciplinary discourse, the interactions remain predominantly applied and case-driven, with limited theoretical integration. By mapping interdisciplinary exchanges, we highlight key gaps and opportunities for greater synthesis across fields. We argue that strengthening the alignment between organizational and technical insights is crucial for advancing DAO research and fostering a more cohesive interdisciplinary framework.
Bishwo Prakash Pokharel, Naresh Kshetri, Suresh Raj Sharma, S. Paudel
The COVID-19 pandemic exposed critical vulnerabilities in global healthcare systems, particularly in data security and interoperability. This paper introduces the blockHealthSecure Framework, which integrates blockchain technology with advanced cybersecurity measures to address these weaknesses and build resilient post-pandemic healthcare systems. Blockchain’s decentralized and immutable architecture enhances the accuracy, transparency, and protection of electronic medical records (EMRs) and sensitive healthcare data. Additionally, it facilitates seamless and secure data sharing among healthcare providers, addressing long-standing interoperability challenges. This study explores the challenges and benefits of blockchain integration in healthcare, with a focus on regulatory and ethical considerations such as HIPAA and GDPR compliance. Key contributions include detailed case studies and examples that demonstrate blockchain’s ability to mitigate risks like ransomware, insider threats, and data breaches. This framework’s design leverages smart contracts, cryptographic hashing, and zero-trust architecture to ensure secure data management and proactive threat mitigation. The findings emphasize the framework’s potential to enhance data security, improve system adaptability, and support regulatory compliance in the face of evolving healthcare challenges. By bridging existing gaps in healthcare cybersecurity, the blockHealthSecure Framework offers a scalable, future-proof solution for safeguarding health outcomes and preparing for global health crises.
Generative artificial intelligence models have brought about advancements in fields like healthcare and finance, as well as in autonomous systems; however, they also encounter notable security vulnerabilities, primarily when operating in cloud environments. These AI models can be targeted by attacks that involve altering input data to deceive the system into generating harmful or incorrect results. This study delves into the security issues that AI systems face in cloud setups, explicitly focusing on the dangers posed by adversarial manipulation of data integrity and the challenges of utilizing shared resources within multi-user environments. The text covers methods for defending AI models, like training and defensive distillation, to make them more robust against attacks. It also delves into security measures for the cloud, such as encrypted communications and robust authentication systems to safeguard data integrity. Furthermore, the importance of AI explainability and transparency in uncovering vulnerabilities and building trust is highlighted. The outcomes of security breaches emphasize the importance of having AI systems to avoid impacts on decision-making and broader ethical and societal concerns. The document also discusses research areas such as quantum algorithms and decentralized security structures to tackle evolving risks and safeguard the future of secure AI applications that generate content.
The virtual world will be altered significantly as a result of the incorporation of metaverses into digital communication. Immersive, cooperative, and resilient 3D cybernetic environments that surpass conventional web surfing define the metaverse. Modern technology and dynamic forces that fortify the metaverse are what propel this advancement, since they allow its hybrid virtual-physical nature to be effortlessly integrated. The development and fulfilment of virtual world technologies require core capabilities including blockchain, artificial intelligence (AI), cloud computing, and 5G and 6G connection. Web3, which uses blockchain technology and smart contracts to create a decentralized, user-centric Internet, is all about the practical and geographical visibility of metaverses. Nonetheless, these ideas are connected to the broader evolution and do not conflict with one another. Because of its multiple functions, the metaverse may be used for a wide range of tasks. The gaming and entertainment sectors employ the metaverse in some of its most well-known uses. Users may enjoy a vibrant and imaginative setting in the metaverse where they can play games, watch films, go to concerts, etc. Because it may offer instructors and students a virtual environment where it is feasible to conduct training and experiments that cannot be experienced in the actual world owing to potential hazards or expenses, the metaverse can have various applications and consequences in the field of education. The metaverse will also benefit corporate growth, employee cooperation and communication, the creation of more realistic simulation models for urban development, process optimization, and many other areas. However, there are drawbacks to the metaverse as well. These include addictiveness, impairment of the ability of the mind to discriminate between actual reality and augmented or virtual reality, privacy protection, safeguarding people&s;s digital identities, information confidentiality, and the requirement for sophisticated hardware and software infrastructure in order to receive, send, simulate, and process information in real time. The Indian Information Technology Act of 2000 and its implementing rules created India&s;s current data protection system, which places requirements on businesses managing sensitive and personal data. Businesses must create organizational safeguards to protect data and get consent before processing any data. As the metaverse integrates more deeply into our digital world, a single legal framework is critical for managing the convergence of artificial intelligence and citizen privacy. In the light of newly introduced Indian Digital Personal Data Protection Act (DPDP Act) of 2023, data fiduciaries, data holders, and data processors have to be cautious of data collection and dissemination, and for this reason, metaverse app developers, app retainers, and app disseminators need special attention. Companies that employ moral artificial intelligence strategies are more prepared to navigate moral and societal traps associated with conducting business in the metaverse.
Smart contracts are programs stored on the blockchain, often developed in a high-level programming language, the most popular of which is Solidity. Smart contracts are used to automate financial transactions and thus bugs can lead to large financial losses. With this paper, we address this problem by describing a verification environment for Solidity in Isabelle/HOL. To this end, we first describe a calculus to reason about Solidity smart contracts. The calculus is formalized in Isabelle/HOL and its soundness is mechanically verified. Then, we verify a theorem which guarantees that all instances of an arbitrary contract type satisfy a corresponding invariant. The theorem can be used to verify invariants for Solidity smart contracts. This is demonstrated by a case study in which we use our approach to verify a simple token implemented in Solidity. Our results show that the framework has the potential to significantly reduce the verification effort compared to verifying directly from the semantics. • We provide a novel calculus to support the verification of Solidity smart contracts. • The calculus is formalized in Isabelle and its soundness is mechanically verified. • We demonstrate the approach by verifying an invariant for an implementation of a token in Solidity.