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1. TEE-Graph: efficient privacy and ownership protection for cloud-based graph spectral analysis

   https://doi.org/10.3389/fdata.2023.1296469  

   Alam, A. K. ; Chen, Keke ( November 2023 , Frontiers in Big Data)

   Big graphs like social network user interactions and customer rating matrices require significant computing resources to maintain. Data owners are now using public cloud resources for storage and computing elasticity. However, existing solutions do not fully address the privacy and ownership protection needs of the key involved parties: data contributors and the data owner who collects data from contributors.

   We propose a Trusted Execution Environment (TEE) based solution: TEE-Graph for graph spectral analysis of outsourced graphs in the cloud. TEEs are new CPU features that can enable much more efficient confidential computing solutions than traditional software-based cryptographic ones. Our approach has several unique contributions compared to existing confidential graph analysis approaches. (1) It utilizes the unique TEE properties to ensure contributors' new privacy needs, e.g., the right of revocation for shared data. (2) It implements efficient access-pattern protection with a differentially private data encoding method. And (3) it implements TEE-based special analysis algorithms: the Lanczos method and the Nystrom method for efficiently handling big graphs and protecting confidentiality from compromised cloud providers.

   The TEE-Graph approach is much more efficient than software crypto approaches and also immune to access-pattern-based attacks. Compared with the best-known software crypto approach for graph spectral analysis, PrivateGraph, we have seen that TEE-Graph has 10³−10⁵times lower computation, storage, and communication costs. Furthermore, the proposed access-pattern protection method incurs only about 10%-25% of the overall computation cost.

   Our experimentation showed that TEE-Graph performs significantly better and has lower costs than typical software approaches. It also addresses the unique ownership and access-pattern issues that other TEE-related graph analytics approaches have not sufficiently studied. The proposed approach can be extended to other graph analytics problems with strong ownership and access-pattern protection.

    

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2. Privacy preserving divisible double auction with a hybridized TEE-blockchain system

   https://doi.org/10.1186/s42400-021-00100-x  

   Liu, Bingyu ; Xie, Shangyu ; Yang, Yuanzhou ; Wang, Rujia ; Hong, Yuan ( December 2021 , Cybersecurity)

   Double auction mechanisms have been designed to trade a variety of divisible resources (e.g., electricity, mobile data, and cloud resources) among distributed agents. In such divisible double auction, all the agents (both buyers and sellers) are expected to submit their bid profiles, and dynamically achieve the best responses. In practice, these agents may not trust each other without a market mediator. Fortunately, smart contract is extensively used to ensure digital agreement among mutually distrustful agents. The consensus protocol helps the smart contract execution on the blockchain to ensure strong integrity and availability. However, severe privacy risks would emerge in the divisible double auction since all the agents should disclose their sensitive data such as the bid profiles (i.e., bid amount and prices in different iterations) to other agents for resource allocation and such data are replicated on all the nodes in the network. Furthermore, the consensus requirements will bring a huge burden for the blockchain, which impacts the overall performance. To address these concerns, we propose a hybridized TEE-Blockchain system (system and auction mechanism co-design) to privately execute the divisible double auction. The designed hybridized system ensures privacy, honesty and high efficiency among distributed agents. The bid profiles are sealed for optimally allocating divisible resources while ensuring truthfulness with a Nash Equilibrium. Finally, we conduct experiments and empirical studies to validate the system and auction performance using two real-world applications.

    

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3. Toward Secure, Privacy-Preserving, and Interoperable Medical Data Sharing via Blockchain

   https://doi.org/10.1109/ICPADS47876.2019.00126  

   Jin, Hao ; Xu, Chen ; Luo, Yan ; Li, Peilong ; Cao, Yu ; Mathew, Jomol ( December 2019 , 2019 IEEE 25th International Conference on Parallel and Distributed Systems (ICPADS))

   In the era of cloud computing and big data analysis, how to efficiently share and utilize medical information scattered across various care providers has become a critical problem. This paper proposes a new framework for sharing medical data in a secure and privacy-preserving way. This framework holistically integrates multi-authority attribute based encryption, blockchain and smart contract, as well as software defined networking to define and enforce sharing policies. Specifically in our framework, patients' medical records are encrypted and stored in hospital databases, where strict access controls are enforced with attribute based encryption coupled with privacy level classification. Our framework leverages blockchain technology to connect scattered private databases from participating hospitals for efficient and secure data provision, smart contracts to enable the business logic of clinical data usage, and software defined networking to revoke sharing privileges. The performance evaluation of our prototype demonstrates that the associated computation costs are reasonable in practice. 

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4. Enabling Secure and Privacy Preserving Identity Management via Smart Contract

   https://doi.org/10.1109/CNS.2019.8802771  

   Liu, Yaoqing ; Sun, Guchuan ; Schuckers, Stephanie ( June 2019 , 2019 IEEE Conference on Communications and Network Security)

   Biometrics have been used increasingly heavily for identity authentication in many critical public services, such as border passes or security check points. However, traditional biometrics-based identity management systems collect and store personal biometrical data in a centralized server or database, and an individual has no control over how her biometrics will be used for what purpose. Such kind of systems can result in serious security and privacy issues for sensitive personal data. In this paper, we design a novel approach to leveraging biometrics and blockchain/smart contract to enable secure and privacy preserving identity management. The basic idea is to use blockchain to store an authority's attestation and the transformed value of an individual's biometrics. The stored data on the blockchain is then controlled by smart contracts which define various access control policies, e.g., access parties, access times, etc. The owner of the biometrical data can flexibly change the access control policies through a white list, a timer and other methods to any identity verifiers. We used the well-known Ethereum platform to implement the proposed approach and tested the effectiveness as well as the flexibility of various access control policies. 

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5. Adding concurrency to smart contracts

   https://doi.org/10.1145/3087801.3087835  

   Thomas Dickerson, Paul Gazzillo ( June 2020 , Distributed computing)

   null (Ed.)

   Modern cryptocurrency systems, such as the Ethereum project, permit complex financial transactions through scripts called smart contracts. These smart contracts are executed many, many times, always without real concurrency. First, all smart contracts are serially executed by miners before appending them to the blockchain. Later, those contracts are serially re-executed by validators to verify that the smart contracts were executed correctly by miners. Serial execution limits system throughput and fails to exploit today’s concurrent multicore and cluster architectures. Nevertheless, serial execution appears to be required: contracts share state, and contract programming languages have a serial semantics. This paper presents a novel way to permit miners and validators to execute smart contracts in parallel, based on techniques adapted from software transactional memory. Miners execute smart contracts speculatively in parallel, allowing non-conflicting contracts to proceed concurrently, and “discovering” a serializable concurrent schedule for a block’s transactions, This schedule is captured and encoded as a deterministic fork-join program used by validators to re-execute the miner’s parallel schedule deterministically but concurrently. We have proved that the validator’s execution is equivalent to miner’s execution. Smart contract benchmarks run on a JVM with ScalaSTM show that a speedup of 1.39× can be obtained for miners and 1.59× for validators with just three concurrent threads. 

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