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Literatura académica sobre el tema "Trusted Execution Environment (TEE)"
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Artículos de revistas sobre el tema "Trusted Execution Environment (TEE)"
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Kato, Fumiyuki, Yang Cao, and Mastoshi Yoshikawa. "PCT-TEE: Trajectory-based Private Contact Tracing System with Trusted Execution Environment." ACM Transactions on Spatial Algorithms and Systems 8, no. 2 (2022): 1–35. http://dx.doi.org/10.1145/3490491.
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Existing Bluetooth-based private contact tracing (PCT) systems can privately detect whether people have come into direct contact with patients with COVID-19. However, we find that the existing systems lack functionality and flexibility , which may hurt the success of contact tracing. Specifically, they cannot detect indirect contact (e.g., people may be exposed to COVID-19 by using a contaminated sheet at a restaurant without making direct contact with the infected individual); they also cannot flexibly change the rules of “risky contact,” such as the duration of exposure or the distance (both spatially and temporally) from a patient with COVID-19 that is considered to result in a risk of exposure, which may vary with the environmental situation. In this article, we propose an efficient and secure contact tracing system that enables us to trace both direct contact and indirect contact. To address the above problems, we need to utilize users’ trajectory data for PCT, which we call trajectory-based PCT . We formalize this problem as a spatiotemporal private set intersection that satisfies both the security and efficiency requirements. By analyzing different approaches such as homomorphic encryption, which could be extended to solve this problem, we identify the trusted execution environment (TEE) as a candidate method to achieve our requirements. The major challenge is how to design algorithms for a spatiotemporal private set intersection under the limited secure memory of the TEE. To this end, we design a TEE-based system with flexible trajectory data encoding algorithms. Our experiments on real-world data show that the proposed system can process hundreds of queries on tens of millions of records of trajectory data within a few seconds.
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Ng, Lucien K. L., Sherman S. M. Chow, Anna P. Y. Woo, Donald P. H. Wong, and Yongjun Zhao. "Goten: GPU-Outsourcing Trusted Execution of Neural Network Training." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 17 (2021): 14876–83. http://dx.doi.org/10.1609/aaai.v35i17.17746.
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Deep learning unlocks applications with societal impacts, e.g., detecting child exploitation imagery and genomic analysis of rare diseases. Deployment, however, needs compliance with stringent privacy regulations. Training algorithms that preserve the privacy of training data are in pressing need. Purely cryptographic approaches can protect privacy, but they are still costly, even when they rely on two or more non-colluding servers. Seemingly-"trivial" operations in plaintext quickly become prohibitively inefficient when a series of them are "crypto-processed," e.g., (dynamic) quantization for ensuring the intermediate values would not overflow. Slalom, recently proposed by Tramer and Boneh, is the first solution that leverages both GPU (for efficient batch computation) and a trusted execution environment (TEE) (for minimizing the use of cryptography). Roughly, it works by a lot of pre-computation over known and fixed weights, and hence it only supports private inference. Five related problems for private training are left unaddressed. Goten, our privacy-preserving training and prediction framework, tackles all five problems simultaneously via our careful design over the "mismatched" cryptographic and GPU data types (due to the tension between precision and efficiency) and our round-optimal GPU-outsourcing protocol (hence minimizing the communication cost between servers). It 1) stochastically trains a low-bitwidth yet accurate model, 2) supports dynamic quantization (a challenge left by Slalom), 3) minimizes the memory-swapping overhead of the memory-limited TEE and its communication with GPU, 4) crypto-protects the (dynamic) model weight from untrusted GPU, and 5) outperforms a pure-TEE system, even without pre-computation (needed by Slalom). As a baseline, we build CaffeScone that secures Caffe using TEE but not GPU; Goten shows a 6.84x speed-up of the whole VGG-11. Goten also outperforms Falcon proposed by Wagh et al., the latest secure multi-server cryptographic solution, by 132.64x using VGG-11. Lastly, we demonstrate Goten's efficacy in training models for breast cancer diagnosis over sensitive images.
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Meftah, Souhail, Shuhao Zhang, Bharadwaj Veeravalli, and Khin Mi Mi Aung. "Revisiting the Design of Parallel Stream Joins on Trusted Execution Environments." Algorithms 15, no. 6 (2022): 183. http://dx.doi.org/10.3390/a15060183.
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The appealing properties of secure hardware solutions such as trusted execution environment (TEE) including low computational overhead, confidentiality guarantee, and reduced attack surface have prompted considerable interest in adopting them for secure stream processing applications. In this paper, we revisit the design of parallel stream join algorithms on multicore processors with TEEs. In particular, we conduct a series of profiling experiments to investigate the impact of alternative design choices to parallelize stream joins on TEE including: (1) execution approaches, (2) partitioning schemes, and (3) distributed scheduling strategies. From the profiling study, we observe three major high-performance impediments: (a) the computational overhead introduced with cryptographic primitives associated with page swapping operations, (b) the restrictive Enclave Page Cache (EPC) size that limits the supported amount of in-memory processing, and (c) the lack of vertical scalability to support the increasing workload often required for near real-time applications. Addressing these issues allowed us to design SecJoin, a more efficient parallel stream join algorithm that exploits modern scale-out architectures with TEEs rendering no trade-offs on security whilst optimizing performance. We present our model-driven parameterization of SecJoin and share our experimental results which have shown up to 4-folds of improvements in terms of throughput and latency.
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Khurshid, Anum, Sileshi Demesie Yalew, Mudassar Aslam, and Shahid Raza. "TEE-Watchdog: Mitigating Unauthorized Activities within Trusted Execution Environments in ARM-Based Low-Power IoT Devices." Security and Communication Networks 2022 (May 25, 2022): 1–21. http://dx.doi.org/10.1155/2022/8033799.
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Trusted execution environments (TEEs) are on the rise in devices all around us ranging from large-scale cloud-based solutions to resource-constrained embedded devices. With the introduction of ARM TrustZone-M, hardware-assisted trusted execution is now supported in IoT nodes. TrustZone-M provides isolated execution of security-critical operations and sensitive data-generating peripherals. However, TrustZone-M, like all other TEEs, does not provide a mechanism to monitor operations in the trusted areas of the device and software in the secure areas of an IoT device has access to the entire secure and nonsecure software stack. This is crucial due to the diversity of device manufacturers and component suppliers in the market, which manifests trust issues, especially when third-party peripherals are incorporated into a TEE. Compromised TEEs can be misused for industrial espionage, data exfiltration through system backdoors, and illegal data sharing. It is of utmost importance here that system peripheral behaviour in terms of resource access is in accordance with their intended usage that is specified during integration. We propose TEE-Watchdog, a lightweight framework that establishes MPU protections for secure system peripherals in TrustZone-enabled low-end IoT devices. TEE-Watchdog ensures blocking unauthorized peripheral accesses and logging of application misbehaviour running in the TEE based on a manifest file. We define lightweight specifications and structure for the application manifest file enlisting permissions for critical system peripherals using concise binary object representation (CBOR). We implement and evaluate TEE-Watchdog using a Musca-A2 test chipboard. Our microbenchmark evaluations on CPU time and RAM usage demonstrated the practicality of TEE-Watchdog. Securing the system peripherals using TEE-Watchdog protections induced a 1.4% overhead on the latency of peripheral accesses, which was 61 microseconds on our test board. Our optimized CBOR-encoded manifest file template also showed a decrease in manifest file size by 40% as compared to the standard file formats, e.g., JSON.
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Chen, Yuehai, Huarun Chen, Shaozhen Chen, et al. "DITES: A Lightweight and Flexible Dual-Core Isolated Trusted Execution SoC Based on RISC-V." Sensors 22, no. 16 (2022): 5981. http://dx.doi.org/10.3390/s22165981.
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A Trusted Execution Environment (TEE) is an efficient way to secure information. To obtain higher efficiency, the building of a dual-core system-on-chip (SoC) with TEE security capabilities is the hottest topic. However, TEE SoCs currently commonly use complex processor cores such as Rocket, resulting in high resource usage. More importantly, the cryptographic unit lacks flexibility and ignores secure communication in dual cores. To address the above problems, we propose DITES, a dual-core TEE SoC based on a Reduced Instruction Set Computer-V (RISC-V). At first, we designed a fully isolated multi-level bus architecture based on a lightweight RISC-V processor with an integrated crypto core supporting Secure Hashing Algorithm-1 (SHA1), Advanced Encryption Standard (AES), and Rivest–Shamir–Adleman (RSA), among which RSA can be configured to five key lengths. Then, we designed a secure boot based on Chain-of-Trust (CoT). Furthermore, we propose a hierarchical access policy to improve the security of inter-core communication. Finally, DITES is deployed on a Kintex 7 Field-Programmable-Gate-Array (FPGA) with a power consumption of 0.297 W, synthesized using TSMC 90 nm. From the results, the acceleration ratios of SHA1 and RSA1024 decryption/encryption can reach 75 and 1331/1493, respectively. Compared to exiting TEE SoCs, DITES has lower resource consumption, higher flexibility, and better security.
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Sebastian, D. Jonathan, Utkarsh Agrawal, Ali Tamimi, and Adam Hahn. "DER-TEE: Secure Distributed Energy Resource Operations Through Trusted Execution Environments." IEEE Internet of Things Journal 6, no. 4 (2019): 6476–86. http://dx.doi.org/10.1109/jiot.2019.2909768.
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Song, Weiqiong, Shuai Guo, Jiwei Li, et al. "Security Authentication Framework Design for Electric Internet of Things." Journal of Physics: Conference Series 2356, no. 1 (2022): 012003. http://dx.doi.org/10.1088/1742-6596/2356/1/012003.
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The intelligent terminal equipment of the electric internet of things (IoT) is vulnerable to network attacks when installing APPs from the application store. Encryption of the communication process can enhance security protection, but the key needs to be stored in the local equipment. When the equipment is attacked, the key is leaked easily resulting in communication security problems. To solve the above problems, we proposes a terminal APP security authentication mechanism based on TrustZone approach and OP-TEE (Open Source Trust Execution Environment) system to identify the identity information of both sides of the communication. The digital certificate of the application store is checked before the terminal equipment installs the APP. After the check is passed, the session key is generated in the Trusted Execution Environment (TEE), and the communication parties use the session key to encrypt the subsequent process. Simulation results validate that the proposed security authentication mechanism can effectively protect the communication process of terminal equipment installation APP and meet the performance requirements.
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Wang, Zhihong, Yongbiao Li, Dingcheng Li, et al. "Enabling Fairness-Aware and Privacy-Preserving for Quality Evaluation in Vehicular Crowdsensing: A Decentralized Approach." Security and Communication Networks 2021 (November 12, 2021): 1–11. http://dx.doi.org/10.1155/2021/9678409.
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With the rapid development of vehicular crowdsensing, it becomes easier and more efficient for mobile devices to sense, compute, and measure various data. However, how to address the fair quality evaluation between the platform and participants while preserving the privacy of solutions is still a challenge. In the work, we present a fairness-aware and privacy-preserving scheme for worker quality evaluation by leveraging the blockchain, trusted execution environment (TEE), and machine learning technologies. Specifically, we build our framework atop the decentralized blockchain which can resist a single point of failure/compromise. The smart contracts paradigm in blockchain enforces correct and automatic program execution for task processing. In addition, machine learning and TEE are utilized to evaluate the quality of data collected by the sensors in a privacy-preserving and fair way, eliminating human subject judgement of the sensing solutions. Finally, a prototype of the proposed scheme is implemented to verify the feasibility and efficiency with a benchmark dataset.
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Noh, Yoongdoo, and Chanik Park. "CrossPay: A TEE (Trusted Execution Environment)-based Offchain Protocol for Real-Time Cross Chain Asset Transfer." KIISE Transactions on Computing Practices 28, no. 3 (2022): 160–74. http://dx.doi.org/10.5626/ktcp.2022.28.3.160.
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Li, Rujia, Qin Wang, Qi Wang, David Galindo, and Mark Ryan. "SoK: TEE-Assisted Confidential Smart Contract." Proceedings on Privacy Enhancing Technologies 2022, no. 3 (2022): 711–31. http://dx.doi.org/10.56553/popets-2022-0093.
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The blockchain-based smart contract lacks privacy, since the contract state and instruction code are exposed to the public. Combining smart-contract execution with Trusted Execution Environments provides an efficient solution, called TEE-assisted smart contracts (TCSC), for protecting the confidentiality of contract states. However, the combination approaches are varied, and a systematic study is absent. Newly released systems may fail to draw upon the experience learned from existing protocols, such as repeating known design mistakes or applying TEE technology in insecure ways. In this paper, we first investigate and categorize existing systems into two types: the layer-one solution and the layer-two solution. Then, we establish an analysis framework to capture their common aspects, covering desired properties (for contract services), threat models, and security considerations (for underlying systems). Based on our taxonomy, we identify their ideal functionalities, and uncover fundamental flaws and challenges in each specification’s design. We believe that this work would provide a guide for the development of TEE-assisted smart contracts, as well as a framework to evaluate future TCSC systems.