Hao Guo

Hao Guo, Collin Meese, Wanxin Li et al.

Federated Learning (FL) is a privacy-preserving machine learning (ML) technology that enables collaborative training and learning of a global ML model based on aggregating distributed local model updates. However, security and privacy guarantees could be compromised due to malicious participants and the centralized FL server. This article proposed a bi-level blockchained architecture for secure federated learning-based traffic prediction. The bottom and top layer blockchain store the local model and global aggregated parameters accordingly, and the distributed homomorphic-encrypted federated averaging (DHFA) scheme addresses the secure computation problems. We propose the partial private key distribution protocol and a partially homomorphic encryption/decryption scheme to achieve the distributed privacy-preserving federated averaging model. We conduct extensive experiments to measure the running time of DHFA operations, quantify the read and write performance of the blockchain network, and elucidate the impacts of varying regional group sizes and model complexities on the resulting prediction accuracy for the online traffic flow prediction task. The results indicate that the proposed system can facilitate secure and decentralized federated learning for real-world traffic prediction tasks.

Wanxin Li, Collin Meese, Hao Guo et al.

The on-demand mobility market, including ridesharing, is becoming increasingly important with e-hailing fares growing at a rate of approximately 130% per annum since 2013. By increasing utilization of existing vehicles and empty seats, ridesharing can provide many benefits including reduced traffic congestion and environmental impact from vehicle usage and production. However, the safety of riders and drivers has become of paramount concern and a method for privacy-preserving identity verification between untrusted parties is essential for protecting users. To this end, we propose a novel privacy-preserving identity verification system, extending zero-knowledge proof (ZKP) and blockchain for use in ridesharing applications. We design a permissioned blockchain network to perform the ZKP verification of a driver's identity, which also acts as an immutable ledger to store ride logs and ZKP records. For the ZKP module, we design a protocol to facilitate user verification without requiring the exchange of any private information. We prototype the proposed system on the Hyperledger Fabric platform, with the Hyperledger Ursa cryptography library, and conduct extensive experimentation. To measure the prototype's performance, we utilize the Hyperledger Caliper benchmark tool to perform extensive analysis and the results show that our system is suitable for use in real-world ridesharing applications.

Hao Guo, Wanxin Li, Ehsan Meamari et al.

The global Electronic Health Record (EHR) market is growing dramatically and has already hit $31.5 billion in 2018. To safeguard the security of EHR data and privacy of patients, fine-grained information access and sharing mechanisms are essential for EHR management. This paper proposes a hybrid architecture of blockchain and edge nodes to facilitate EHR management. In this architecture, we utilize attribute-based multi-signature (ABMS) scheme to authenticate user's signatures without revealing the sensitive information and multi-authority attribute-based encryption (ABE) scheme to encrypt EHR data which is stored on the edge node. We develop the blockchain module on Hyperledger Fabric platform and the ABMS module on Hyperledger Ursa library. We measure the signing and verifying time of the ABMS scheme under different settings, and experiment with the authentication events and access activities which are logged as transactions in blockchain.

Ehsan Meamari, Hao Guo, Chien-Chung Shen et al.

Attribute-based Encryption (ABE) is an information centric security solution that moves beyond traditional restrictions of point-to-point encryption by allowing for flexible, fine-grain policy-based and content-based access control that is cryptographically enforced. As the original ABE systems are managed by a single authority, several efforts have decentralized different ABE schemes to address the key escrow problem, where the authority can issue secret keys to itself to decrypt all the ciphertext. However, decentralized ABE (DABE) schemes raise the issue of collusion attacks. In this paper, we review two existing types of collusion attacks on DABE systems, and introduce a new type of collusion among authorities and data users. We show that six existing DABE systems are vulnerable to the newly introduced collusion and propose a model to secure one of the DABE schemes.

Ehsan Meamari, Hao Guo, Chien-Chung Shen et al.

Attribute-Based Encryption (ABE) has emerged as an information-centric public-key cryptographic system which allows a data owner to share data, according to access policy, with multiple data users based on the attributes they possess, without knowing their identities. In the original ABE schemes, a central authority administrates the system and issues secret keys to data users based on their attributes and both the owner and users need to trust a specific CA. However, in certain real-world applications, the data users would not trust anyone but themselves. For such situations, we introduce a new decentralization model of ABE, termed Data User-based ABE (DU-ABE), which is managed jointly by the data users. DU-ABE is the first decentralized ABE scheme that replaces the authorities with the data users without employing any other extra entities.

Hao Guo, Wanxin Li, Mark Nejad et al.

The global Electronic Health Record (EHR) market is growing dramatically and expected to reach $39.7 billions by 2022. To safe-guard security and privacy of EHR, access control is an essential mechanism for managing EHR data. This paper proposes a hybrid architecture to facilitate access control of EHR data by using both blockchain and edge node. Within the architecture, a blockchain-based controller manages identity and access control policies and serves as a tamper-proof log of access events. In addition, off-chain edge nodes store the EHR data and apply policies specified in Abbreviated Language For Authorization (ALFA) to enforce attribute-based access control on EHR data in collaboration with the blockchain-based access control logs. We evaluate the proposed hybrid architecture by utilizing Hyperledger Composer Fabric blockchain to measure the performance of executing smart contracts and ACL policies in terms of transaction processing time and response time against unauthorized data retrieval.

Hao Guo, Ehsan Meamari, Chien-Chung Shen

Attribute-based access control makes access control decisions based on the assigned attributes of subjects and the access policies to protect objects by mediating operations from the subjects. Authority, which validates attributes of subjects, is one key component to facilitate attribute-based access control. In an increasingly decentralized society, multiple attributes possessed by subjects may need to be validated by multiple different authorities. This paper proposes a multi-authority attribute-based access control scheme by using Ethereum's smart contracts. In the proposed scheme, Ethereum smart contracts are created to define the interactions between data owner, data user, and multiple attribute authorities. A data user presents its attributes to different attribute authorities, and after successful validation of attributes, obtains attribute tokens from respective attribute authorities. After collecting enough attribute tokens, a smart contract will be executed to issue secret key to the data user to access the requested object. The smart contracts for multi-authority attribute-based access control have been prototyped in Solidity, and their performance has been evaluated on the Rinkeby Ethereum Testnet.