Deploying blockchains to simplify AI algorithm auditing

Artificial Intelligence has largely occupied various sectors in the world. A huge number of business companies have incorporated several machine learning algorithms for day-to-day decision making. With increasing applications of AI algorithms, the concerns regarding its outcomes have also increased due to bias. In AI algorithms, bias occurs due to multiple reasons including incomplete data, skewed data, human error and so on. These algorithms have the tendency to amplify partially and discrimination in the results instead of benefiting them. This makes it compulsory for the algorithms to be audited. Currently, AI algorithm auditing processes have several challenges including tendency of biases to be deeply ingrained into the system, making these difficult to mitigate; lack of transparency in decision making and many more. This study presents the emerging technology of blockchains to be a viable solution to the existing problem. It comprehensively discusses the suitability of blockchains for transparency in the process of algorithm auditing which is bound to easily capture the issue and the layer consisting it. Consequently, the process of algorithm auditing will be more convenient and more productive. Moreover, this review also discusses some potential challenges that need to be addressed and some future recommendations for this integration

Blockchain for Healthcare: Securing Patient Data and Enabling Trusted Artificial Intelligence

Advances in information technology are digitizing the healthcare domain with the aim of improved medical services, diagnostics, continuous monitoring using wearables, etc., at reduced costs. This digitization improves the ease of computation, storage and access of medical records which enables better treatment experiences for patients. However, it comes with a risk of cyber attacks and security and privacy concerns on this digital data. In this work, we propose a Blockchain based solution for healthcare records to address the security and privacy concerns which are currently not present in existing e-Health systems. This work also explores the potential of building trusted Artificial Intelligence models over Blockchain in e-Health, where a transparent platform for consent-based data sharing is designed. Provenance of the consent of individuals and traceability of data sources used for building and training the AI model is captured in an immutable distributed data store. The audit trail of the data access captured using Blockchain provides the data owner to understand the exposure of the data. It also helps the user to understand the revenue models that could be built on top of this framework for commercial data sharing to build trusted AI models

Web3.0 Security: Privacy Enhancing and Anonym Auditing in Blockchain-based Structures

The advent of Web 3.0, underpinned by blockchain technologies, promises to transform the internet's landscape by empowering individuals with decentralized control over their data. However, this evolution brings unique security challenges that need to be addressed. This paper explores these complexities, focusing on enhancing privacy and anonymous auditing within blockchain structures. We present the architecture of Web 3.0 based on the blockchain, providing a clear perspective on its workflow and security mechanisms. A security protocol for Web 3.0 systems, employing privacy-preserving techniques and anonymous auditing during runtime, is proposed. Key components of our solution include the integration of privacy-enhancing techniques and the utilization of Tor for anonymous auditing. We discuss related work and propose a framework that meets these new security requirements. Lastly, we offer an evaluation and comparison of our model to existing methods. This research contributes towards the foundational understanding of Web 3.0's secure structure and offers a pathway towards secure and privacy-preserving digital interactions in this novel internet landscape

Federated Learning Attacks and Defenses: A Survey

In terms of artificial intelligence, there are several security and privacy deficiencies in the traditional centralized training methods of machine learning models by a server. To address this limitation, federated learning (FL) has been proposed and is known for breaking down ``data silos" and protecting the privacy of users. However, FL has not yet gained popularity in the industry, mainly due to its security, privacy, and high cost of communication. For the purpose of advancing the research in this field, building a robust FL system, and realizing the wide application of FL, this paper sorts out the possible attacks and corresponding defenses of the current FL system systematically. Firstly, this paper briefly introduces the basic workflow of FL and related knowledge of attacks and defenses. It reviews a great deal of research about privacy theft and malicious attacks that have been studied in recent years. Most importantly, in view of the current three classification criteria, namely the three stages of machine learning, the three different roles in federated learning, and the CIA (Confidentiality, Integrity, and Availability) guidelines on privacy protection, we divide attack approaches into two categories according to the training stage and the prediction stage in machine learning. Furthermore, we also identify the CIA property violated for each attack method and potential attack role. Various defense mechanisms are then analyzed separately from the level of privacy and security. Finally, we summarize the possible challenges in the application of FL from the aspect of attacks and defenses and discuss the future development direction of FL systems. In this way, the designed FL system has the ability to resist different attacks and is more secure and stable.Comment: IEEE BigData. 10 pages, 2 figures, 2 table

Data trust framework using blockchain and smart contracts

Lack of trust is the main barrier preventing more widespread data sharing. The lack of transparent and reliable infrastructure for data sharing prevents many data owners from sharing their data. Data trust is a paradigm that facilitates data sharing by forcing data controllers to be transparent about the process of sharing and reusing data. Blockchain technology has the potential to present the essential properties for creating a practical and secure data trust framework by transforming current auditing practices and automatic enforcement of smart contracts logic without relying on intermediaries to establish trust. Blockchain holds an enormous potential to remove the barriers of traditional centralized applications and propose a distributed and transparent administration by employing the involved parties to maintain consensus on the ledger. Furthermore, smart contracts are a programmable component that provides blockchain with more flexible and powerful capabilities. Recent advances in blockchain platforms toward smart contracts' development have revealed the possibility of implementing blockchain-based applications in various domains, such as health care, supply chain and digital identity. This dissertation investigates the blockchain's potential to present a framework for data trust. It starts with a comprehensive study of smart contracts as the main component of blockchain for developing decentralized data trust. Interrelated, three decentralized applications that address data sharing and access control problems in various fields, including healthcare data sharing, business process, and physical access control system, have been developed and examined. In addition, a general-purpose application based on an attribute-based access control model is proposed that can provide trusted auditability required for data sharing and access control systems and, ultimately, a data trust framework. Besides auditing, the system presents a transparency level that both access requesters (data users) and resource owners (data controllers) can benefit from. The proposed solutions have been validated through a use case of independent digital libraries. It also provides a detailed performance analysis of the system implementation. The performance results have been compared based on different consensus mechanisms and databases, indicating the system's high throughput and low latency. Finally, this dissertation presents an end-to-end data trust framework based on blockchain technology. The proposed framework promotes data trustworthiness by assessing input datasets, effectively managing access control, and presenting data provenance and activity monitoring. A trust assessment model that examines the trustworthiness of input data sets and calculates the trust value is presented. The number of transaction validators is defined adaptively with the trust value. This research provides solutions for both data owners and data users’ by ensuring the trustworthiness and quality of the data at origin and transparent and secure usage of the data at the end. A comprehensive experimental study indicates the presented system effectively handles a large number of transactions with low latency

Editor’s Note. Towards Blockchain Intelligence

In this special issue, we want to gather some innovative applications that are currently pushing forward the research on Blockchain technologies. In particular, we are interested also in those applications that put the focus on the data, enabling new processes that are able to leverage relevant knowledge from the data. This special issue will be successful if readers gain a better understanding on how Blockchain can be applied to very diverse areas, and might even be interested in designing, implementing and deploying an innovative solution to a completely different field of knowledge. We hope this Special Issue can provide a better understanding and key insights to readers on how Blockchain and artificial intelligence are cross-fertilizing to revolutionize many aspects in our societies