RK
R.G. Kromes
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1
This research looks at two open-source tools for differential privacy: Google's Differential Privacy Library and the OpenDP Library. The main aim of this study is to test them side-by-side and observe how they compared quantitatively. Specifically, the focus is on their implementations of the Laplace noise mechanism. It measured their computational performance, scalability and the accuracy of their results (utility). These experiments were done for basic Count and Sum queries on synthetic structured datasets, using different privacy settings (ϵ) and dataset sizes.
The experiments using the Laplace mechanism showed some clear differences. GoogleDP was consistently faster than OpenDP for both Count and Sum queries and this speed advantage was especially noticeable with larger datasets. The choice of ϵ did not really change how long either library took to run. When it came to utility, both libraries showed the expected pattern: more privacy (lower ϵ) meant less accurate results. The accuracy differences between the two were small. For Sum queries OpenDP gave results with slightly better utility, especially on large datasets and when ϵ was small (meaning more privacy). This paper offers current, data-supported information about the practical pros and cons when using these libraries for these common DP operations with the Laplace mechanism. A full comparison including the Gaussian mechanism could not be completed in this study due to the fact that PyDP (GoogleDP's python wrapper) had only the Laplace mechanism implemented. This remains an important task for future work.
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This research looks at two open-source tools for differential privacy: Google's Differential Privacy Library and the OpenDP Library. The main aim of this study is to test them side-by-side and observe how they compared quantitatively. Specifically, the focus is on their implementations of the Laplace noise mechanism. It measured their computational performance, scalability and the accuracy of their results (utility). These experiments were done for basic Count and Sum queries on synthetic structured datasets, using different privacy settings (ϵ) and dataset sizes.
The experiments using the Laplace mechanism showed some clear differences. GoogleDP was consistently faster than OpenDP for both Count and Sum queries and this speed advantage was especially noticeable with larger datasets. The choice of ϵ did not really change how long either library took to run. When it came to utility, both libraries showed the expected pattern: more privacy (lower ϵ) meant less accurate results. The accuracy differences between the two were small. For Sum queries OpenDP gave results with slightly better utility, especially on large datasets and when ϵ was small (meaning more privacy). This paper offers current, data-supported information about the practical pros and cons when using these libraries for these common DP operations with the Laplace mechanism. A full comparison including the Gaussian mechanism could not be completed in this study due to the fact that PyDP (GoogleDP's python wrapper) had only the Laplace mechanism implemented. This remains an important task for future work.
Differential Privacy (DP) has become one of the most used approaches to protect individual data. However, its implementation can vary significantly depending on the context we are using it. In this study, we aim to compare two such implementations of DP: Google's Differential Privacy, an open-source library used for structured data analytics, and Differentially Private Offsite Prompt Tuning (DP-OPT), a tool used for the adaptation of machine learning models.
The main research question of this study is the following: "How do DP-OPT and Google's Differential Privacy Library compare when accounting for different factors in different contexts?" We aim to conduct this research by doing a literature-based study where no empirical experiments will be performed. This is because of the underlying complexity of both tools, especially DP-OPT, and the time constraints posed in this project.
The main results of this study show that Google DP is a tool that benefits from its interpretability and speed when processing data analysis, while DP-OPT shows a higher accuracy when training large language models (LLMs). Meaning that there is no single mechanism for each problem, but instead it depends on factors like the underlying task complexity, current available resources, and the final goal of the task.
By comparing these tools side-by-side, this research aims to provide more insights into how each tool behaves and performs under different contexts and tasks. We aim to guide developers and researchers to make better and more informed decisions when choosing a tool for their desired task. ...
The main research question of this study is the following: "How do DP-OPT and Google's Differential Privacy Library compare when accounting for different factors in different contexts?" We aim to conduct this research by doing a literature-based study where no empirical experiments will be performed. This is because of the underlying complexity of both tools, especially DP-OPT, and the time constraints posed in this project.
The main results of this study show that Google DP is a tool that benefits from its interpretability and speed when processing data analysis, while DP-OPT shows a higher accuracy when training large language models (LLMs). Meaning that there is no single mechanism for each problem, but instead it depends on factors like the underlying task complexity, current available resources, and the final goal of the task.
By comparing these tools side-by-side, this research aims to provide more insights into how each tool behaves and performs under different contexts and tasks. We aim to guide developers and researchers to make better and more informed decisions when choosing a tool for their desired task. ...
Differential Privacy (DP) has become one of the most used approaches to protect individual data. However, its implementation can vary significantly depending on the context we are using it. In this study, we aim to compare two such implementations of DP: Google's Differential Privacy, an open-source library used for structured data analytics, and Differentially Private Offsite Prompt Tuning (DP-OPT), a tool used for the adaptation of machine learning models.
The main research question of this study is the following: "How do DP-OPT and Google's Differential Privacy Library compare when accounting for different factors in different contexts?" We aim to conduct this research by doing a literature-based study where no empirical experiments will be performed. This is because of the underlying complexity of both tools, especially DP-OPT, and the time constraints posed in this project.
The main results of this study show that Google DP is a tool that benefits from its interpretability and speed when processing data analysis, while DP-OPT shows a higher accuracy when training large language models (LLMs). Meaning that there is no single mechanism for each problem, but instead it depends on factors like the underlying task complexity, current available resources, and the final goal of the task.
By comparing these tools side-by-side, this research aims to provide more insights into how each tool behaves and performs under different contexts and tasks. We aim to guide developers and researchers to make better and more informed decisions when choosing a tool for their desired task.
The main research question of this study is the following: "How do DP-OPT and Google's Differential Privacy Library compare when accounting for different factors in different contexts?" We aim to conduct this research by doing a literature-based study where no empirical experiments will be performed. This is because of the underlying complexity of both tools, especially DP-OPT, and the time constraints posed in this project.
The main results of this study show that Google DP is a tool that benefits from its interpretability and speed when processing data analysis, while DP-OPT shows a higher accuracy when training large language models (LLMs). Meaning that there is no single mechanism for each problem, but instead it depends on factors like the underlying task complexity, current available resources, and the final goal of the task.
By comparing these tools side-by-side, this research aims to provide more insights into how each tool behaves and performs under different contexts and tasks. We aim to guide developers and researchers to make better and more informed decisions when choosing a tool for their desired task.
Homomorphic Encryption (HE) enables computation directly on encrypted data, while offering strong cryptographic and privacy guarantees for data-driven sectors like healthcare, finance, and cloud computing. However, practical adoption of HE is severely limited by its computational overhead and specialized expertise requirements. This thesis investigates the financial feasibility of HE by analyzing its Total Cost of Ownership (TCO), an overlooked but crucial factor for researchers, companies, and cloud service customers deciding whether to adopt HE. Building on performance benchmarks from Guillot et al. and Lo et al., we evaluate the runtime, resource requirements, and personnel costs of Fully Homomorphic Encryption (FHE) across different workloads, and Amazon Web Service (AWS) cloud configurations. Our results show that encryption and expert labor dominate the cost, with encryption accounting for over 80% of the runtime and personnel expertise exceeding 98% of the total annual cost ($125,558.066). These findings highlight that, despite the security benefits of HE, its financial overhead restricts its adoption to organizations with substantial cryptographic expertise and budgets. This thesis equips researchers and companies with data-driven insight into HE's true economic impact and underlines the need for further optimization and automation to make HE accessible and cost-effective for broader, real-world use.
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Homomorphic Encryption (HE) enables computation directly on encrypted data, while offering strong cryptographic and privacy guarantees for data-driven sectors like healthcare, finance, and cloud computing. However, practical adoption of HE is severely limited by its computational overhead and specialized expertise requirements. This thesis investigates the financial feasibility of HE by analyzing its Total Cost of Ownership (TCO), an overlooked but crucial factor for researchers, companies, and cloud service customers deciding whether to adopt HE. Building on performance benchmarks from Guillot et al. and Lo et al., we evaluate the runtime, resource requirements, and personnel costs of Fully Homomorphic Encryption (FHE) across different workloads, and Amazon Web Service (AWS) cloud configurations. Our results show that encryption and expert labor dominate the cost, with encryption accounting for over 80% of the runtime and personnel expertise exceeding 98% of the total annual cost ($125,558.066). These findings highlight that, despite the security benefits of HE, its financial overhead restricts its adoption to organizations with substantial cryptographic expertise and budgets. This thesis equips researchers and companies with data-driven insight into HE's true economic impact and underlines the need for further optimization and automation to make HE accessible and cost-effective for broader, real-world use.
Quantum SMPC: Rich in theory, limited in practice
A systematic review of quantum secure multi-party computation
Secure Multi-Party Computation (SMPC) is a widely-used cryptographic tool for privacy-preserving data analysis. The progress in the field of quantum computing has led to the development of Quantum SMPC (QSMPC), which promises informationtheoretic security based on physics laws. There is a significant number of proposed protocols. However, as the number of theoretical protocols grows, their practical viability remains unclear. Our paper presents a systematic literature review of 37 recent QSMPC protocols to assess the state of the field, focusing on protocol functionality, quantum resource requirements, privacy-ensuring techniques, and feasibility on current technologies. Our analysis reveals a plethora of innovative theoretically-private protocols, constrained by significant practical issues. We found that protocols tend to rely on a core set of quantum resources, including entangled states (Bell, GHZ), Quantum Key Distribution, and decoy particles to ensure security. We also observe a frequent reliance on a semi-honest third party. Most importantly, we identify a noticeable gap in feasibility. 19 out of 37 of the papers surveyed provide no clear discussion on the practicality of the proposed protocols. The papers that do discuss this topic describe small-scale simulations, which may require algorithmic compromises and are tested in ideal conditions only. Our findings highlight a gap between theory and practice, suggesting that real-world application of the protocols is not yet possible, given the current state of quantum hardware.
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Secure Multi-Party Computation (SMPC) is a widely-used cryptographic tool for privacy-preserving data analysis. The progress in the field of quantum computing has led to the development of Quantum SMPC (QSMPC), which promises informationtheoretic security based on physics laws. There is a significant number of proposed protocols. However, as the number of theoretical protocols grows, their practical viability remains unclear. Our paper presents a systematic literature review of 37 recent QSMPC protocols to assess the state of the field, focusing on protocol functionality, quantum resource requirements, privacy-ensuring techniques, and feasibility on current technologies. Our analysis reveals a plethora of innovative theoretically-private protocols, constrained by significant practical issues. We found that protocols tend to rely on a core set of quantum resources, including entangled states (Bell, GHZ), Quantum Key Distribution, and decoy particles to ensure security. We also observe a frequent reliance on a semi-honest third party. Most importantly, we identify a noticeable gap in feasibility. 19 out of 37 of the papers surveyed provide no clear discussion on the practicality of the proposed protocols. The papers that do discuss this topic describe small-scale simulations, which may require algorithmic compromises and are tested in ideal conditions only. Our findings highlight a gap between theory and practice, suggesting that real-world application of the protocols is not yet possible, given the current state of quantum hardware.
FATE vs. SecretFlow: A Practical Comparison for Privacy-Preserving Machine Learning
Privacy-Preserving Data Analytics
Secure multi-party computation (SMPC) is a cryptographic technique that enables multiple parties to work together on data without sharing their private information with each other. This paper investigates how two open-source frameworks, SecretFlow and FATE, implement SMPC and other privacy-preserving techniques and evaluates their usability and scalability in applied settings. The goal is to better understand the trade-offs these frameworks offer for researchers and developers seeking to build privacy-preserving machine learning pipelines. Using a small-scale experimental setup, both frameworks were deployed in controlled environments and evaluated based on setup complexity, documentation quality, modularity, architecture, and the secure computation methods used. The results show that SecretFlow offers greater SMPC flexibility, modularity, and developer usability, making it well-suited for research and rapid prototyping. FATE, while more complex to integrate, provides comprehensive workflow coordination and is better suited for production environments. Experiments were conducted on a single physical node with a small dataset, enabling reproducibility but limiting generalizability to large-scale, real-world deployments. This work provides practical insights into the usability and architectural design of the frameworks, along with a protocol-level comparison to guide developers in selecting tools for privacy-sensitive machine learning.
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Secure multi-party computation (SMPC) is a cryptographic technique that enables multiple parties to work together on data without sharing their private information with each other. This paper investigates how two open-source frameworks, SecretFlow and FATE, implement SMPC and other privacy-preserving techniques and evaluates their usability and scalability in applied settings. The goal is to better understand the trade-offs these frameworks offer for researchers and developers seeking to build privacy-preserving machine learning pipelines. Using a small-scale experimental setup, both frameworks were deployed in controlled environments and evaluated based on setup complexity, documentation quality, modularity, architecture, and the secure computation methods used. The results show that SecretFlow offers greater SMPC flexibility, modularity, and developer usability, making it well-suited for research and rapid prototyping. FATE, while more complex to integrate, provides comprehensive workflow coordination and is better suited for production environments. Experiments were conducted on a single physical node with a small dataset, enabling reproducibility but limiting generalizability to large-scale, real-world deployments. This work provides practical insights into the usability and architectural design of the frameworks, along with a protocol-level comparison to guide developers in selecting tools for privacy-sensitive machine learning.
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.
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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.
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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. ...
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. ...
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.
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.
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. ...
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. ...
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.
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.
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.
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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.