Threshold signatures play a crucial role in the security of blockchain applications. An efficient threshold signature can be applied to enhance the security of wallets and transactions by enforcing multi-device-based authentication, as this requires adversaries to compromise more devices to recover the key. Additionally, threshold signatures can protect user privacy, for instance, by enabling anonymous transaction signing on behalf of a group of users sharing a blockchain wallet. This study conducts a comprehensive analysis of threshold signature schemes, identifying FROST as the premier choice due to its performance efficiency, improved energy consumption, and practical feasibility on mid-range IoT devices and smartphones as demonstrated through empirical testing. Furthermore, we introduce a protocol for integrating FROST with Hyperledger Fabric v3.0, aimed at enhancing IoT devices' ability to interact with blockchain networks through efficient transaction signing. Our experiments reveal that an IoT network of five devices, under optimal network conditions, can sign a transaction and commit it to the Hyperledger Fabric network within 3.2 seconds, leveraging a 2-second batch timeout configuration.

The Machine Learning (ML) technology has taken the world by storm since it equipped the machines with previously unimaginable decision-making capabilities. However, building powerful ML models is not an easy task, but the demand for their utilization in different industries and areas of expertise is high. This was recognized by entities that have managed to create ML models and they started offering ML prediction services to clients in exchange for financial compensation. In this work, we explore how a ML predication service platform can be built in which we focus on two things: (1) privacy-preservation which entails keeping the client’s datasets and service provider’s ML models private and (2) inference verifiability ensuring that the ML prediction service providers do not commit fraud. The result are two platforms: ML Prediction Service Platform (MLPSP) which does not protect the secrecy of the client’s datasets but offers model privacy and verifiability of the predictions and Input-Privacy ML Prediction Service Platform (IP-MLPSP) which protects the secrecy of the client’s dataset and model privacy but the verifiability is probabilistic.

Blockchain technology has revolutionized the way data is stored, managed, and shared across various industries. Its decentralized nature and immutability make it highly attractive in use cases that require transparency, integrity, and accountability. However, some applications demand confidentiality, necessitating the development of permissioned/consortium blockchains that try to strike a balance between transparency and privacy. Hyperledger Fabric is a permissioned blockchain that has gained popularity due to its modular architecture, performance, and scalability. Despite its strengths, the current set of privacy-enhancing features leaves room for improvement.

Therefore, this master thesis aims to explore the potential of introducing various privacy-enhancing technologies to Hyperledger Fabric, including Dynamic Searchable Symmetric Encryption (DSSE), Multi Authority Attribute-based Encryption (MA-ABE), and Trusted Execution Environments (TEE). Based on the promising results of our study, we decided to implement DSSE and MA-ABE. The combination of blockchain technology with TEE was ruled out after the thorough analysis of two research papers on the subject.

Our main result is the first implementation of a provable secure DSSE scheme in the context of consortium blockchains. Moreover, we developed a blockchain-enabled MA-ABE system that utilizes a novel and generic approach to foreign function invocation. Finally, the thesis discusses unexplored challenges in the field of logistics related to electronic consignment notes used in road transport. To address these issues, we designed a blockchain architecture that incorporates our developed privacy-enhanced technologies.

This paper offers a prototype of a smart-contract-based encryption scheme meant to improve the security of user data being uploaded to the ledger. A new extension to the self-encryption scheme was introduced by integrating identity into the encryption process. Such integration allows to permanently preserve ownership of the original file and link it to the person who originally encrypted it. Moreover, self-encryption provides strong security guarantees under the condition that the encrypted file and the key are safely stored.

Blockchain technologies allow users to securely store and trace their data on a fully decentralized system, and have the potential to make a huge impact on many industries. While traditional, permissionless blockchains such as Bitcoin, Ethereum, and Cardano are very popular, they are currently unable to provide trust and privacy on the network. To solve these issues, many new, permissioned blockchain technologies have been implemented, including Hyperledger Fabric. Although Hyperledger Fabric has proven to be highly successful in providing trust and privacy through the use of identities, channels, and private data collections, one of its major drawbacks is the lack of a flexible and scalable access control system. Currently, access control decisions have to be built into each smart contract individually, which can cause many vulnerabilities and prevent access policies to be updated dynamically.

This research aims to answer the research question “How can secure access control in Hyperledger Fabric be guaranteed by combining multiple ID’s, attributes, and policies with the components that regulate access control?”. To answer this question, the access control system currently used by Hyperledger Fabric is first completely analyzed. Next, a new implementation is proposed that builds upon the existing solution but provides users and developers with easier ways to make access control decisions based on combinations of multiple ID's, attributes, and policies. This solution is then implemented using the smart contract technology of Hyperledger Fabric, which allows it to easily be deployed to existing Hyperledger Fabric networks. Finally, the performance impact of this proposed implementation is analyzed, and new areas of research are proposed that can potentially be explored in future papers.

At the end of this research, it was concluded that it is possible to combine multiple ID's, attributes, and policies with the help of Hyperledger Fabric's smart contract technology. Furthermore, it could be seen that the performance impact for real-world applications is negligible compared to the insecure case of always providing access to a resource without performing access control.