With the rapid development of big data and the gradual maturity of artificial intelligence technology, the combination of big data and artificial intelligence has become increasingly close and complementary, bringing huge opportunities and challenges to the development and progress of various fields. Big data provides massive amounts of data, and artificial intelligence can use this data to conduct analysis in aspects such as deep learning, pattern recognition, and intelligent decision-making, thereby generating useful information and value. However, the combination of big data and artificial intelligence also faces many data security issues such as data leakage, data privacy protection, and model security. Effective measures must be taken to ensure data security during data storage, data processing, and data transfer.
Therefore, Intel teamed up with Inspur Information to build an end-to-end big data and artificial intelligence privacy protection machine learning solution. This solution provides a Trusted Execution Environment (TEE) based on Intel Software Guard Extensions (SGX), using Inspur Information Yunhai Insight provides big data capability support and establishes end-to-end big data and artificial intelligence privacy computing solutions through BigDL-PPML. The joint testing and application practice of Intel Big-PPML and Inspur Cloud Insight have proven the security, efficiency and excellent performance of this solution.
Data security issues when big data and artificial intelligence are combined
The combination of big data and artificial intelligence is one of the hot areas of current technological development, and its application scope involves many fields such as business, medical care, education, and energy. However, in the process of combining big data and artificial intelligence, data security issues cannot be ignored. The following data security issues are mainly faced:
Data privacy protection: The scale and type of data processed by the combination of big data and artificial intelligence are very large and complex, including a large amount of personally identifiable information, financial information, medical information and other sensitive information. If this information is leaked or used by unauthorized third parties, Access will cause great losses to individuals, businesses and institutions. Therefore, measures must be taken to protect data privacy, such as data encryption, data desensitization and other technologies.
Data leakage: The information stored in big data often includes sensitive information such as business secrets and personal privacy. If this information is obtained by attackers, it will cause heavy losses to enterprises and individuals. Therefore, measures must be taken to prevent data leakage, such as network encryption, authentication and access control technologies.
Fake data attacks: Data quality in big data is often not completely guaranteed, and attackers can manipulate models by submitting false data, thereby causing damage to enterprises or institutions. Therefore, measures must be taken to prevent fake data attacks, such as data quality management, anomaly detection, trusted execution environment and other technologies.
Model security issues: In artificial intelligence, models are often trained based on big data. If an attacker can access the model, a large amount of sensitive information can be obtained from it. Therefore, measures must be taken to protect model security, such as data encryption, access control and other technologies.
Intel BigDL-PPML joins hands with Inspur Information Yunhai Insight for end-to-end privacy protection machine learning
In order to solve the data security problems encountered when combining big data and artificial intelligence, Intel cooperated with Inspur Information to support the highly secure Inspur Information KOS operating system based on Intel SGX trusted execution environment technology and use the Inspur Information Cloud Sea Container Cloud Platform ICKS ( InCloud K8S, ICKS) deploys Kubernetes (K8s) clusters with one click. Inspur Information Yunhai Insight big data platform provides operation and maintenance management, data storage, data calculation, permission management and other capabilities, and realizes end-to-end big data and artificial intelligence through Intel BigDL-PPML. End-to-end privacy-preserving machine learning solution.
Figure 1 Big data and artificial intelligence end-to-end privacy-preserving machine learning solution architecture
Inspur Information KOS is a server operating system independently developed based on open source technologies such as Linux Kernel and OpenAnolis. It supports mainstream architecture processors such as x86 and ARM, and its performance and stability are industry-leading. It can meet the needs of application scenarios such as cloud computing, big data, distributed storage, artificial intelligence, and edge computing.
Inspur Information KOS server operating system is developed and enhanced based on the open source OpenAnolis system, adding self-developed software to provide a full range of (kernel and user mode) operating system support, with core stability, security, compatibility and performance. Its capabilities have been fully verified. It is a server operating system with strong security, high availability, high reliability, high performance, and easy maintenance. It can provide enterprise users with a trustworthy infrastructure platform and meet the needs of enterprise users in multiple application scenarios. This solution uses the Inspur Information KOS system to strengthen the entire end-to-end privacy protection process, and Inspur Information KOS has its own SGX driver, which can simplify the deployment and implementation process.
Figure 2 KOS product architecture
Inspur Information Cloud Sea Container Cloud Platform (InCloud K8S, ICKS for short) is an enterprise-level container cloud platform. Based on container and Kubernetes container orchestration technology, it adopts a microservice architecture and is application-centric. It provides comprehensive application management, Cloud platform services such as service grid, intelligent monitoring and operation and maintenance, DevOps, heterogeneous device management, application migration, disaster recovery and backup, multi-tenant management, and security auditing can help enterprises accelerate application migration to the cloud and achieve high availability and elastic scalability of business. , and perform automated management of the entire application life cycle. This solution uses the Inspur Cloud Container Cloud Platform ICKS to deploy a K8S cluster environment with one click, simplifying the deployment process, improving deployment efficiency, ensuring high cluster availability, and providing a stable and reliable container scheduling environment for this solution.
Figure 3 ICKS product architecture
BigDL-PPML is a distributed privacy-preserving machine learning platform built by Intel's open source big data and artificial intelligence application platform BigDL, mainly based on Intel SGX trusted execution environment technology. BigDL-PPML enables companies to explore powerful artificial intelligence technologies while minimizing the security risks associated with handling large amounts of sensitive data. PPML effectively protects data in storage, in transit, and in use: compute and memory protected by SGX Enclaves, storage protected by encryption, network communications protected by remote authentication and transport layer security, and optional federated learning support.
Figure 4 BigDL PPML product architecture
Cloud Sea Insight
Yunhai Insight is Inspur Information's enterprise-level big data basic software. It integrates the industry's mainstream new big data processing technologies and includes more than 30 big data components such as data collection, data storage, data calculation, retrieval services, orchestration, data lake, and data security. It provides unified platform management and operation, realizes in-depth function enhancement and performance optimization, and can help customers easily cope with application scenarios such as massive data collection, storage, calculation, query, analysis and data security. As an important part of AI computing, BigDL PPML is integrated into the Insight big data platform, providing Spark SQL, ML/DL, federated learning and other functions to improve the AI service capabilities of the Insight platform.
Figure 5 Insight product architecture
Safely and efficiently explore the value of data
The end-to-end big data and artificial intelligence privacy protection machine learning solution built by Intel BigDL-PPML and Inspur Information Yunhai Insight can help enterprises implement big data and AI such as data analysis, machine learning, and deep learning while protecting data security. application. By integrating Intel BigDL-PPML, Inspur Cloud Insight can provide enterprises with more secure and reliable big data and artificial intelligence privacy computing solutions, thus bringing the following benefits:
Data privacy protection: Privacy-preserving multi-party computing technology can distribute data across multiple computing nodes for calculation and complete computing tasks without leaking the original data. This can effectively protect the data privacy of enterprises and prevent sensitive data from being leaked.
Efficiency and scalability: Intel BigDL-PPML is implemented based on the distributed computing framework Spark, which can realize the training and inference of large-scale deep learning models while ensuring computing efficiency and scalability. This can provide enterprises with more efficient and reliable data analysis and processing services.
Reduce data processing costs: Inspur Information Yunhai Insight big data platform has complete data collection, data storage, and data calculation processes. Through big data processing technology and data security system, massive data can be processed in one stop. This can reduce data transmission and processing costs for enterprises while improving data security.
Increase data value: By leveraging Intel BigDL-PPML for training and inference, enterprises can analyze data more accurately, thereby increasing the value of data. At the same time, data privacy is protected, companies can share data with more confidence, and data sharing and cooperation within the industry are promoted.
To sum up, Intel BigDL-PPML joins hands with Inspur Information Yunhai Insight to establish an end-to-end big data and artificial intelligence privacy computing solution, which can bring many benefits to enterprises, including data privacy protection, efficiency and scalability, and reduction of Data processing costs and increasing data value, etc.
Continuously update and improve innovative applications of privacy computing solutions in various industries
Based on Intel SGX technology, Intel BigDL-PPML and Inspur Information Yunhai Insight can provide enterprises with a more secure and reliable big data and artificial intelligence privacy protection machine learning solution. After joint testing and multi-party practice by both parties, this solution can not only protect users' Privacy is secure, and it can improve the machine learning efficiency of enterprises, bringing many benefits to enterprises, including data privacy protection, efficiency and scalability, reducing data processing costs and increasing data value.
With the continuous improvement of computing power and the continuous innovation of artificial intelligence algorithms, the application scenarios of big data and artificial intelligence will become more extensive, and data privacy and security issues will become increasingly important. Intel BigDL-PPML based on Intel SGX technology will become an important tool for enterprises to achieve data security and privacy protection. Inspur Information Yunhai Insight big data intelligent analysis platform will be continuously updated and improved to provide enterprises with more secure and reliable data processing and analysis services. . The two parties will continue to cooperate in depth to further improve the end-to-end big data and artificial intelligence privacy protection solution. While ensuring the security of user data, they will promote the sustainable development of big data and artificial intelligence technology and generate more innovations and applications in various industries. .