Intel Software Guard Extensions Research Articles

PPPCT: Privacy-Preserving framework for Parallel Clustering Transcriptomics data

Single-cell transcriptomics data provides crucial insights into patients’ health, yet poses significant privacy concerns. Genomic data privacy attacks can have deep implications, encompassing not only the patients’ health information but also extending widely to compromise their families’. Moreover, the permanence of leaked data exacerbates the challenges, making retraction an impossibility. While extensive efforts have been directed towards clustering single-cell transcriptomics data, addressing critical challenges, especially in the realm of privacy, remains pivotal. This paper introduces an efficient, fast, privacy-preserving approach for clustering single-cell RNA-sequencing (scRNA-seq) datasets. The key contributions include ensuring data privacy, achieving high-quality clustering, accommodating the high dimensionality inherent in the datasets, and maintaining reasonable computation time for big-scale datasets. Our proposed approach utilizes the map-reduce scheme to parallelize clustering, addressing intensive calculation challenges. Intel Software Guard eXtension (SGX) processors are used to ensure the security of sensitive code and data during processing. Additionally, the approach incorporates a logarithm transformation as a preprocessing step, employs non-negative matrix factorization for dimensionality reduction, and utilizes parallel k-means for clustering. The approach fully leverages the computing capabilities of all processing resources within a secure private cloud environment. Experimental results demonstrate the efficacy of our approach in preserving patient privacy while surpassing state-of-the-art methods in both clustering quality and computation time. Our method consistently achieves a minimum of 7% higher Adjusted Rand Index (ARI) than existing approaches, contingent on dataset size. Additionally, due to parallel computations and dimensionality reduction, our approach exhibits efficiency, converging to very good results in less than 10 seconds for a scRNA-seq dataset with 5000 genes and 6000 cells when prioritizing privacy and under two seconds without privacy considerations.Availability and implementationCode and datasets availability: https://github.com/University-of-Windsor/PPPCT.

GUARDIAN: A Hardware-Assisted Distributed Framework to Enhance Deep Learning Security

The ubiquity of artificial intelligence (AI) has led to its extensive research and application in various fields, such as computer vision, natural language processing, and medical image analysis. However, responsible AI faces severe security challenges, including the leakage of pretrained models and valuable training data. The existing solutions adopt new algorithm designs (such as federated learning) or cryptography (such as homomorphic encryption) to prevent possible security vulnerabilities. We observe that hardware-assisted trusted execution environments (TEEs) can further improve machine learning responsibility. Intel Software Guard Extension (SGX) is a popular, trusted execution hardware that enables users’ programs to run in an untrusted execution environment, such as a malicious operating system, but ensures the confidentiality and integrity of data. Therefore, we have designed, a hardware-assisted secure machine learning training framework that protects data security during the training process. We have analyzed the typical characteristics of machine learning applications and characterized through extensive experiments. Our findings demonstrate that introducing security guarantees causes performance degradation, which provides a feasible optimization direction in the near future.

Enhancing the Security and Privacy in the IoT Supply Chain Using Blockchain and Federated Learning with Trusted Execution Environment

Federated learning has emerged as a promising technique for the Internet of Things (IoT) in various domains, including supply chain management. It enables IoT devices to collaboratively learn without exposing their raw data, ensuring data privacy. However, federated learning faces the threats of local data tampering and upload process attacks. This paper proposes an innovative framework that leverages Trusted Execution Environment (TEE) and blockchain technology to address the data security and privacy challenges in federated learning for IoT supply chain management. Our framework achieves the security of local data computation and the tampering resistance of data update uploads using TEE and the blockchain. We adopt Intel Software Guard Extensions (SGXs) as the specific implementation of TEE, which can guarantee the secure execution of local models on SGX-enabled processors. We also use consortium blockchain technology to build a verification network and consensus mechanism, ensuring the security and tamper resistance of the data upload and aggregation process. Finally, each cluster can obtain the aggregated parameters from the blockchain. To evaluate the performance of our proposed framework, we conducted several experiments with different numbers of participants and different datasets and validated the effectiveness of our scheme. We tested the final global model obtained from federated training on a test dataset and found that increasing both the number of iterations and the number of participants improves its accuracy. For instance, it reaches 94% accuracy with one participant and five iterations and 98.5% accuracy with ten participants and thirty iterations.

Securing TEEs With Verifiable Execution Contracts

Recent works have demonstrated that trusted execution environments, such as Intel Software Guard Extensions, are vulnerable to various attacks from the privileged software, including side-channel attacks. Existing solutions, such as T-SGX, Déjà Vu, Cloak and Varys, detect side-channel attacks at runtime. But they are limited by design, because false detection is unavoidable in these detection methods and therefore any security policy developed atop these mechanisms has to tolerate some malicious operations to achieve practical false positive detection rates. In this paper, we propose the concept of verifiable execution contracts, which request the privileged software to provide a benign execution environment for enclaves within which launching attacks becomes very difficult, if not impossible. Since the privileged software is untrusted, we design methods for verifying that the execution contracts are observed. With the proposed verifiable execution contracts, we analyzed how existing attacks could be mitigated.

Securing Federated Learning Based on Blockchain Technology

A blockchain-based federated learning approach with secure aggregation in a trusted execution environment for the Internet of Things (IoT). The proposed approach aims to address the privacy and security concerns associated with federated learning in IoT environments. The approach involves using a blockchain to store the learning model and to maintain a distributed ledger of transactions. The learning model is trained on local IoT devices using federated learning techniques, with each device contributing its local data. The aggregation of the model updates is performed securely within a trusted execution environment, using homomorphism encryption and secret sharing techniques. The proposed approach offers several advantages over traditional federated learning approaches, including improved privacy and security, increased scalability, and enhanced trustworthiness. It also enables the creation of a decentralized and democratic learning environment, where each device has an equal say in the learning process. The approach is evaluated using a real-world dataset, and the results demonstrate its effectiveness in terms of accuracy and privacy preservation. The paper concludes that the proposed approach has the potential to enable secure and scalable federated learning in IoT environments, with applications in healthcare, smart cities, and other domains. This paper offers a blockchain-based federated learning (FL) framework with an Intel Software Guard Extension (SGX)-based trusted execution environment (TEE) for safely aggregating local models in the Industrial Internet of Things (IoT) Local models in FL can be modified with by attackers. As a result, a global model derived from manipulated local models may be incorrect. As a result, the proposed system makes use of a blockchain network to secure model aggregation. Each blockchain node contains an SGX-enabled CPU that secures the FL-based aggregating processes required to construct a global model. Blockchain nodes may validate the aggregated model's validity, perform a blockchain consensus method to secure the model's integrity, and add it to the distributed ledger for tamper-proof storage. . Before utilising the aggregated model, each cluster can acquire it from the blockchain and validate its fidelity. To assess the performance of the proposed system, we ran many experiments using various CNN models and datasets

Blockchain-Based Federated Learning With Secure Aggregation in Trusted Execution Environment for Internet-of-Things

This paper proposes a blockchain-based Federated Learning (FL) framework with Intel Software Guard Extension (SGX)-based Trusted Execution Environment (TEE) to securely aggregate local models in Industrial Internet-of-Things (IIoTs). In FL, local models can be tampered with by attackers. Hence, a global model generated from the tampered local models can be erroneous. Therefore, the proposed framework leverages a blockchain network for secure model aggregation. Each blockchain node hosts an SGX-enabled processor that securely performs the FL-based aggregation tasks to generate a global model. Blockchain nodes can verify the authenticity of the aggregated model, run a blockchain consensus mechanism to ensure the integrity of the model, and add it to the distributed ledger for tamper-proof storage. Each cluster can obtain the aggregated model from the blockchain and verify its integrity before using it. We conducted several experiments with different CNN models and datasets to evaluate the performance of the proposed framework.

Trust Beyond Border: Lightweight, Verifiable User Isolation for Protecting In-Enclave Services.

Due to the absence of in-enclave isolation, today's trusted execution environment (TEE), specifically Intel's Software Guard Extensions (SGX), does not have the capability to securely run different users' tasks within a single enclave, which is required for supporting real-world services, such as an in-enclave machine learning model that classifies the data from various sources, or a microservice (e.g., data search) that performs a very small task (within sub-seconds) for a user and therefore cannot afford the resources and the delay for creating a separate enclave for each user. To address this challenge, we developed Liveries, a technique that enables lightweight, verifiable in-enclave user isolation for protecting time-sharing services. Our approach restricts an in-enclave thread's privilege when configuring an enclave, and further performs integrity check and sanitization on critical enclave data upon user switches. For this purpose, we developed a novel technique that ensures the protection of sensitive user data (e.g., session keys) even in the presence of the adversary who may have compromised the enclave. Our study shows that the new technique is lightweight (1% overhead) and verifiable (about 3200 lines of code), making a step towards assured protection of real-world in-enclave services.

GDPR-Aware Revocable P2P File-Sharing System Over Consortium Blockchain

With the rise of blockchain technology, the peer-to-peer (P2P) network system has once again caught people's attention to equipping a blockchain with a big storage capacity. In the traditional P2P file-sharing network systems, such as InterPlanetary File System (IPFS), data stored in the other nodes cannot be revoked by the owner and can only be removed by other nodes themselves. To comply with the criteria of the European Union's General Data Protection Regulation, it is important to ensure that personal data can be completely removed by their owners. To improve the privacy and security of the P2P file-sharing system, we propose a revocable and monitorable P2P file-sharing system over a consortium blockchain to achieve revocation of files in the decentralized environment. By using a trusted execution environment, such as Intel Software Guard Extensions (SGX), the proposed scheme can verify the integrity of the executables of the P2P file-sharing system and generate a file authentication code for each IPFS node to make sure that the system is synchronized correctly. This scheme elaborately integrates the autonomous smart contracts and Intel SGX hardware to obtain the monitorable merit. The experimental results suggest that enhancing the security and privacy take modest computing costs into consideration. To the best of our knowledge, this scheme is the first attempt to achieve the P2P file-sharing system with securely revocable functions.

Integrity of virtual testing for crash protection

The interest in virtual testing is globally rapidly increasing because of several advantages compared to physical tests in laboratories. In the area of passive car safety, finite element simulations can be used to get further insights, use more biofidelic human models and make the overall assessment more robust by incorporating more variety in the virtual testing load cases. For a successful implementation of virtual testing in regulations or consumer information, the integrity of the procedure has to be ensured. As car simulation models used within the virtual testing are usually not shared with the evaluation institutions due to intellectual property (IP) issues, this is a challenging task. Stringent validation and certification procedures are needed and it has to be ensured that the models used in these steps are the same as the ones used for the virtual testing. In this paper, we developed a secure procedure for model version control. Through analysis of possible threats for both sides, car manufacturer and evaluation institution, we defined requirements, which the new procedure should satisfy. These requirements state that the integrity and authenticity of all shared documents should be protected, as well as the confidentiality of the simulation model. By considering all prerequisites, we developed an architecture for a new procedure. This architecture uses cryptographic algorithms such as hash functions and digital signatures to ensure integrity and authenticity, as well as secure computation mechanisms such as Intel Software Guard Extensions (SGX). In our proof-of-concept implementation, we demonstrated how a secure wrapper around LS-DYNA can produce a signed report to authenticate the input model files based on a hash tree and link them to the simulation results. The evaluation institution can use a matching verification tool to verify that the models were not manipulated compared to other simulation runs or the qualification process. The developed procedure can be used for trustworthy implementation of virtual testing into consumer information or regulation for the assessment of car safety with strengthened integrity. Further research is needed to develop comparable procedures for other simulation software packages or ideally integrate it directly into the simulation software.

FaultMorse: An automated controlled-channel attack via longest recurring sequence

With the rise of the concept of Trusted Execution Environments (TEEs), such as Intel Software Guard Extensions (SGX), researchers are prompted to constantly verify its effectiveness. Controlled-channel attacks are proposed to construct side channels against the shielding systems by intentionally provoking page faults. So far, various powerful and noise-free controlled-channel attacks have been introduced. However, there are some challenges encountered in the actual practice of these attacks, e.g., extensive manual effort is always required to analyze the target binary and identify conditional control-flow patterns.In this paper, we present FaultMorse, an automated controlled-channel attack. We adopt a global perspective to analyze the page fault sequence and find a specific recurring pattern that corresponds to some specific instructions in the program. Most of the secret bits can be automatically deduced by analyzing the locations of the recurring pattern in the page fault sequence. Compared to previous works, FaultMorse can reduce the complexity of analysis. We propose a method to control page fault counts to improve the attack performance. We implement our FaultMorse attack on a physical machine and evaluate its effectiveness, universality, and page-fault rate. The experimental results show that for some known vulnerable algorithms, FaultMorse can automatically deduce more than 99% of the secret bits.

Privacy‐enhancing machine learning framework with private aggregation of teacher ensembles

Private aggregation of teacher ensembles (PATE), a general machine learning framework based on knowledge distillation, can provide a privacy guarantee for training data sets. However, this framework poses a number of security risks. First, PATE mainly focuses on the privacy of teachers' training data and fails to protect the privacy of their students' data. Second, PATE relies heavily on a trusted aggregator to count teachers' votes, which is not convincing enough to assume a third party would never leak teachers' votes during the knowledge transfer process. To address the abovementioned issues, we improve the original PATE framework and present a new one that combines secret sharing with Intel Software Guard Extensions in a novel way. In the proposed framework, teachers are trained locally, then uploaded and stored in two computing servers in the form of secret shares. In the knowledge transfer phase, the two computing servers receive shares of private inputs from students before collaboratively performing secure predictions. Thus neither teachers nor students expose sensitive information. During the aggregation process, we propose an effective masking technique suitable for the setting to keep the prediction results private and prevent the votes from being leaked to the aggregation server. Besides, we optimize the aggregation mechanism and add noise perturbations adaptively based on the posterior entropy of the prediction results. Finally, we evaluate the performance of the new framework on multiple data sets and experimentally demonstrate that the new framework allows highly efficient, accurate, and secure predictions.

A Blockchain-Based and SGX-Enabled Access Control Framework for IoT

With the rapid development of physical networks, tens of billions of Internet of Things (IoT) devices have been deployed worldwide. Access control is essential in the IoT system, which manages user access to vital IoT data. However, access control for the IoT is mainly based on centralized trusted servers, which face problems such as a single point of failure and data leakage. To tackle these challenges, we propose an access control framework for the IoT by combining blockchain and Intel software guard extension (SGX) technology. A blockchain validates both IoT devices and edge servers added to the network. The access control contract is deployed on the blockchain, which can manage attribute-based access control policies in a fine-grained manner and make access control decisions flexibly. SGX technology is introduced into the edge computing server to realize the confidentiality of data processing. Finally, we implemented the prototype of the framework on Quorum and conducted extensive experiments and theoretical analyses on the performance of the blockchain. The results of the experimental tests and theoretical analyses show that our framework has more advantages in computing costs and on-chain storage costs.

SGXDump: A Repeatable Code-Reuse Attack for Extracting SGX Enclave Memory

Intel SGX (Software Guard Extensions) is a hardware-based security solution that provides a trusted computing environment. SGX creates an isolated memory area called enclave and prevents any illegal access from outside of the enclave. SGX only allows executables already linked statically to the enclave when compiling executables to access its memory, so code injection attacks to SGX are not effective. However, as a previous study has demonstrated, Return-Oriented Programming (ROP) attacks can overcome this defense mechanism by injecting a series of addresses of executable codes inside the enclave. In this study, we propose a novel ROP attack, called SGXDump, which can repeat the attack payload. SGXDump consists only of gadgets in the enclave and unlike previous ROP attacks, the SGXDump attack can repeat the attack payload, communicate with other channels, and implement conditional statements. We successfully attacked two well-known SGX projects, mbedTLS-SGX and Graphene-SGX. Based on our attack experiences, it seems highly probable that an SGXDump attack can leak the entire enclave memory if there is an exploitable memory corruption vulnerability in the target SGX application.

SGXAP: SGX-Based Authentication Protocol in IoV-Enabled Fog Computing

With the maturity and popularization of the Internet of Things, we saw the emergence of the Internet of Vehicles. This collects and processes real-time traffic information, alleviates traffic congestion, and realizes intelligent transportation. However, sensitive information, such as real-time driving data of vehicles, are transmitted on public channels, which are easily to steal and manipulate for attackers. In addition, vehicle communications are vulnerable to malicious attacks. Therefore, it is essential to design secure and efficient protocols. Many studies have adopted asymmetric cryptosystems and fog computing to in this environment, but most of them do not reflect the advantages of fog nodes, which share the computational burden of cloud servers. Therefore, it is challenging to design a protocol that effectively uses fog nodes. In this paper, we design an authentication protocol based on a symmetric encryption algorithm and fog computing in the Internet of Vehicles. In this protocol, we first propose a four-layer architecture that significantly reduces the computational burden of cloud servers. To resist several well-known attacks, we also apply Intel software guard extensions to our protocol. This is because it can resist privileged insider attacks. We prove the security of the proposed protocol through the Real-Or-Random model and informal analysis. We also compare the performance of the proposed protocol with recent protocols. The results show better security and a lower computational cost.

An internet of secure and private things: A service-oriented architecture

Low-cost networked IoT devices are fast becoming commonplace. From implanted medical devices to motion-activated surveillance cameras and from driverless smart cars to voice-operated home management systems, IoT devices continue to permeate further and deeper into our lives. However, this widespread adoption of IoT devices has also given rise to a wide range of security, privacy and trust issues that are unique to the IoT ecosystem. Conventional solutions are ill-suited for the IoT domain due to limited resources, network dynamics, and evolving trust boundaries. Hence, novel mechanisms are needed to address the specific challenges of the IoT landscape. To this end, we propose a user-centric cloud-based service that allows device owners to have fine-grained control over what kind and how much data is shared through their IoT devices. Our scheme builds on top of Intel Software Guard Extensions (SGX) to instantiate secure virtual clones (shadows) of actual devices in the cloud, substantially reducing the attack surface for IoT networks. Furthermore, a scalable infrastructure in the cloud allows us to deploy sophisticated policy enforcement and data scrubbing mechanisms on a per application basis giving users explicit control over data sharing. The presented approach requires little effort on part of device vendors and users as the service provider handles the bulk of the work. We demonstrate the effectiveness of our approach empirically by implementing the service on SGX hardware and deploying advanced data cleansing policies on device-generated data.

A Secure Authentication Scheme for Wireless Sensor Networks Based on DAC and Intel SGX

Wireless sensor networks (WSNs) are widely implemented in military, intelligent medical, intelligent transportation, space exploration, and other fields. However, the authentication and communication of WSNs are carried out in the harsh external environment via a public channel, which is more vulnerable to various attacks than the traditional networks. Authentication is the key technology of security measures, but a common authentication scheme is not suitable for WSNs due to limitations of memory, computing, and energy consumption. Therefore, designing a secure and efficient authentication scheme is essential in WSNs. In recent years, several studies proved that the dynamic authentication credential (DAC) is efficient for enhancing the security of the authentication scheme. This article designs a secure authentication scheme for WSNs based on DAC and Intel software guard extensions (SGX). Compared with other DAC authentication schemes, our scheme provides the confirmation action of DAC rotation, which can effectively prevent the asynchronous update problem caused by packet loss. In order to resist the privileged user attack and the authentication table leakage attack, we choose the SGX, which can protect the data in use, as the trusted execution environment in the gateway node, and we adopt SGX to store the master key for protecting the authentication table. Finally, the security of our authentication scheme is verified by BAN logic, the simulation tool AVISPA, and informal security analysis. Through the consumption overhead analysis, the NS3 simulation result, and detailed comparison with other recent schemes, we conclude that our scheme is practical and achieves better security with less overhead.

Faulty Point Unit: ABI Poisoning Attacks on Trusted Execution Environments

This article analyzes a previously overlooked attack surface that allows unprivileged adversaries to impact floating-point computations in enclaves through the Application Binary Interface (ABI). In a comprehensive study across 7 industry-standard and research enclave shielding runtimes for Intel Software Guard Extensions (SGX), we show that control and state registers of the x87 Floating-Point Unit (FPU) and Intel Streaming SIMD Extensions are not always properly sanitized on enclave entry. We furthermore show that this attack goes beyond the x86 architecture and can also affect RISC-V enclaves. Focusing on SGX, we abuse the adversary’s control over precision and rounding modes as an ABI fault injection primitive to corrupt enclaved floating-point operations. Our analysis reveals that this is especially relevant for applications that use the older x87 FPU, which is still under certain conditions used by modern compilers. We exemplify the potential impact of ABI quality-degradation attacks for enclaved machine learning and for the SPEC benchmarks. We then explore the impact on confidentiality, showing that control over exception masks can be abused as a controlled channel to recover enclaved multiplication operands. Our findings, affecting 5 of 7 studied SGX runtimes and one RISC-V runtime, demonstrate the challenges of implementing high-assurance trusted execution across computing architectures.

Cooperative Detection Method for DDoS Attacks Based on Blockchain

Distributed Denial of Service (DDoS) attacks is always one of the major problems for service providers. Using blockchain to detect DDoS attacks is one of the current popular methods. However, the problems of high time overhead and cost exist in the most of the blockchain methods for detecting DDoS attacks. This paper proposes a blockchain-based collaborative detection method for DDoS attacks. First, the trained DDoS attack detection model is encrypted by the Intel Software Guard Extensions (SGX), which provides high security for uploading the DDoS attack detection model to the blockchain. Secondly, the service provider uploads the encrypted model to Inter Planetary File System (IPFS) and then a corresponding Content-ID (CID) is generated by IPFS which greatly saves the cost of uploading encrypted models to the blockchain. In addition, due to the small amount of model data, the time cost of uploading the DDoS attack detection model is greatly reduced. Finally, through the blockchain and smart contracts, the CID is distributed to other service providers, who can use the CID to download the corresponding DDoS attack detection model from IPFS. Blockchain provides a decentralized, trusted and tamper-proof environment for service providers. Besides, smart contracts and IPFS greatly improve the distribution efficiency of the model, while the distribution of CID greatly improves the efficiency of the transmission on the blockchain. In this way, the purpose of collaborative detection can be achieved, and the time cost of transmission on blockchain and IPFS can be considerably saved. We designed a blockchain-based DDoS attack collaborative detection framework to improve the data transmission efficiency on the blockchain, and use IPFS to greatly reduce the cost of the distribution model. In the experiment, compared with most blockchain-based method for DDoS attack detection, the proposed model using blockchain distribution shows the advantages of low cost and latency. The remote authentication mechanism of Intel SGX provides high security and integrity, and ensures the availability of distributed models.

Blockchain and SGX-Enabled Edge-Computing-Empowered Secure IoMT Data Analysis

The Internet of Medical Things (IoMT) is an important application of the Internet of Things (IoT) in the health field, including remote health monitoring and remote medical diagnosis. This not only brings convenience to the patient but also reduces the cost of the patient. However, the surge of data brought by mobile health monitoring equipment challenges the traditional centralized data processing model. In particular, medical data are closely related to patient privacy. Therefore, only part of the specific medical data should be provided to the medical institutions in need, rather than all the data, to ensure the confidentiality of the data to the greatest extent. But curious data processing centers can easily lead to data leakage. To tackle these challenges, we use edge computing and blockchain to build a new framework. In particular, the trusted execution environment, namely, software guard extension (SGX) technology, is introduced into edge computing to ensure the confidentiality of the data analysis process. The blockchain authenticates the IoMT devices and cloud service providers that are added to the network and provides an access policy management mechanism for IoMT data. Moreover, a prototype of the proposed framework is implemented using Hyperledger Fabric and Intel SGX, and the analysis of the blockchain and SGX performance are also presented.

HySec-Flow: Privacy-Preserving Genomic Computing with SGX-based Big-Data Analytics Framework.

Trusted execution environments (TEE) such as Intel's Software Guard Extension (SGX) have been widely studied to boost security and privacy protection for the computation of sensitive data such as human genomics. However, a performance hurdle is often generated by SGX, especially from the small enclave memory. In this paper, we propose a new Hybrid Secured Flow framework (called "HySec-Flow") for large-scale genomic data analysis using SGX platforms. Here, the data-intensive computing tasks can be partitioned into independent subtasks to be deployed into distinct secured and non-secured containers, therefore allowing for parallel execution while alleviating the limited size of Page Cache (EPC) memory in each enclave. We illustrate our contributions using a workflow supporting indexing, alignment, dispatching, and merging the execution of SGX- enabled containers. We provide details regarding the architecture of the trusted and untrusted components and the underlying Scorn and Graphene support as generic shielding execution frameworks to port legacy code. We thoroughly evaluate the performance of our privacy-preserving reads mapping algorithm using real human genome sequencing data. The results demonstrate that the performance is enhanced by partitioning the time-consuming genomic computation into subtasks compared to the conventional execution of the data-intensive reads mapping algorithm in an enclave. The proposed HySec-Flow framework is made available as an open-source and adapted to the data-parallel computation of other large-scale genomic tasks requiring security and scalable computational resources.