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What data encryption technologies are being used at rest and in transit? What is the typical retention time for this data at the edge? How often do you expect to access this data at the edge?
Edge Computing
Edge computing often uses similar encryption technologies as cloud computing. At rest, data may be encrypted using symmetric or asymmetric algorithms like AES or RSA. In transit, encrypted protocols like HTTPS or TLS are often used.
1. Encryption technologies for data at rest: AES, DES, RSA,
CONTROL QUESTION: What data encryption technologies are being used at rest and in transit?
Big Hairy Audacious Goal (BHAG) for 10 years from now: In ten years, a Big Hairy Audacious Goal (BHAG) for Edge Computing could be to have achieved 100% end-to-end data encryption, utilizing advanced quantum-resistant algorithms, for both data at rest and in transit, enabling maximum security and privacy for all users and devices in a highly interconnected and heterogeneous edge computing landscape.
Advanced data encryption technologies that could be used include:
1. Quantum-resistant encryption algorithms, such as:
t* New Hope: a post-quantum key-exchange algorithm based on the Ring-LWE problem
t* Frodo: a post-quantum public-key encryption scheme based on the Learning-with-Errors problem
t* NTRU: a public-key cryptosystem based on the shortest vector problem in a lattice
2. Homomorphic Encryption: a technique that allows computations to be performed on encrypted data without decryption, thus preserving confidentiality and integrity of the data.
3. Secure Multi-party Computation (SMPC): a method that enables multiple parties to jointly compute a function over their inputs while keeping those inputs private.
4. Confidential Computing: a technology that isolates and encrypts code and data in use, ensuring data confidentiality and integrity even when running in untrusted environments.
5. Trusted Execution Environments (TEEs): secure areas within a processor that provide an isolated environment for code and data, protecting them from unauthorized access or modification.
Achieving this BHAG would require significant advancements in cryptographic research, as well as widespread adoption and standardization of these advanced encryption technologies within the edge computing ecosystem. It would also demand close collaboration among industry, academia, and government organizations to ensure interoperability, security, and privacy for all stakeholders.
"I used this dataset to personalize my e-commerce website, and the results have been fantastic! Conversion rates have skyrocketed, and customer satisfaction is through the roof."
Synopsis:
The rapid growth of Internet of Things (IoT) devices and the need for real-time data processing have driven the adoption of edge computing. This case study focuses on a manufacturing client who aimed to secure their data in transit and at rest in their edge computing environment. The consulting methodology involved the implementation of data encryption technologies, employee training, and policy development.
Consulting Methodology:
The consulting process began with a thorough assessment of the client′s current edge computing infrastructure. The assessment included an analysis of the existing data encryption technologies, employee training, and data management policies.
Next, the consulting team developed a customized solution that incorporated data encryption technologies for data at rest and in transit. The solution included the implementation of Advanced Encryption Standard (AES) 256-bit encryption for data at rest and Transport Layer Security (TLS) 1.3 for data in transit.
The consulting team also provided employee training on best practices for data management and security. The training included topics such as password management, phishing prevention, and incident response.
Finally, the consulting team worked with the client to develop a comprehensive data management policy that outlined roles and responsibilities for data security, incident response procedures, and data backup and recovery plans.
Deliverables:
The deliverables for this project included:
1. Data encryption technologies for data at rest and in transit
2. Employee training materials and resources
3. Data management policy template
4. Implementation plan and timeline
Implementation Challenges:
The implementation of data encryption technologies posed several challenges. One challenge was the integration of the encryption technologies with existing systems and devices. The consulting team worked closely with the client′s IT team to ensure seamless integration.
Another challenge was the training of employees on the new data management policies and procedures. The consulting team addressed this challenge by providing comprehensive training materials and resources and conducting regular follow-up sessions to ensure employee understanding and compliance.
KPIs:
The key performance indicators (KPIs) for this project included:
1. Reduction in data breaches
2. Increase in employee compliance with data management policies
3. Decrease in incident response time
4. Improvement in data backup and recovery processes
Market Considerations:
The edge computing market is expected to grow at a compound annual growth rate (CAGR) of 37.4% from 2020 to 2027 (Source: Fortune Business Insights, 2021). As the market continues to grow, it is crucial for organizations to prioritize data security in their edge computing environments.
Citations:
1. Fortune Business Insights. (2021). Edge Computing Market Size, Share u0026 COVID-19 Impact Analysis, By Component (Hardware, Software, and Services), By End-use Industry (Manufacturing, Energy u0026 Utilities, Transportation u0026 Logistics, Healthcare, and Others), and Regional Forecast, 2020-2027.
2. IBM. (2021). Advanced Encryption Standard (AES). Retrieved from u003chttps://www.ibm.com/support/pages/what-advanced-encryption-standard-aesu003e
3. Mozilla. (2021). TLS 1.3. Retrieved from u003chttps://wiki.mozilla.org/Security/Features/TLS1.3u003e
In conclusion, this case study demonstrates the importance of data encryption technologies in securing edge computing environments. By implementing AES 256-bit encryption for data at rest and TLS 1.3 for data in transit, the manufacturing client was able to enhance their data security and protect sensitive information. Additionally, employee training and comprehensive data management policies further strengthened the organization′s overall data security posture.
Do you struggle to find the most relevant and urgent questions to ask in order to get results in these ever-evolving fields? Look no further!
Our Edge Computing and High Performance Computing Knowledge Base has been carefully curated to meet your needs and provide you with the most crucial information.
With over 1500 prioritized requirements, solutions, benefits, and case studies, our dataset is the ultimate resource for any individual or organization seeking to excel in Edge Computing and High Performance Computing.
But what makes us stand out from the rest?Unlike our competitors, we have taken the time to prioritize and categorize the most important questions and considerations in both edge computing and high performance computing.
This means that you can easily access the information you need, without wasting time sifting through irrelevant data.
Our Edge Computing and High Performance Computing Knowledge Base is designed specifically for professionals like yourself who need quick and accurate information to make informed decisions.
With our product, you can expect to receive in-depth knowledge and insights that will elevate your understanding of these complex fields and help you achieve outstanding results.
Moreover, our dataset offers an affordable alternative to costly research and consulting services.
You can access all the information you need at a fraction of the cost, without compromising quality.
Plus, with its DIY approach, our Edge Computing and High Performance Computing Knowledge Base gives you the freedom to explore and use the data in a way that best suits your needs.
Still not convinced? Let us tell you about the benefits of our product.
Our dataset not only provides a comprehensive overview of edge computing and high performance computing, but it also offers detailed specifications and examples to enhance your understanding.
It is the perfect tool for businesses seeking to make strategic decisions and stay ahead of the competition.
At an unbeatable cost, our Edge Computing and High Performance Computing Knowledge Base offers you a wealth of information, including both the pros and cons of these technologies.
It is a one-stop-shop for all your edge computing and high performance computing needs.
So don’t miss out on this exclusive opportunity to gain a competitive edge in edge computing and high performance computing.
Our dataset is what you need to take your knowledge and results to the next level.
Invest in our Edge Computing and High Performance Computing Knowledge Base today and unlock the potential of these game-changing technologies.
Discover Insights, Make Informed Decisions, and Stay Ahead of the Curve:
Key Features:
Comprehensive set of 1524 prioritized Edge Computing requirements. - Extensive coverage of 120 Edge Computing topic scopes.
- In-depth analysis of 120 Edge Computing step-by-step solutions, benefits, BHAGs.
- Detailed examination of 120 Edge Computing case studies and use cases.
- Digital download upon purchase.
- Enjoy lifetime document updates included with your purchase.
- Benefit from a fully editable and customizable Excel format.
- Trusted and utilized by over 10,000 organizations.
- Covering: Service Collaborations, Data Modeling, Data Lake, Data Types, Data Analytics, Data Aggregation, Data Versioning, Deep Learning Infrastructure, Data Compression, Faster Response Time, Quantum Computing, Cluster Management, FreeIPA, Cache Coherence, Data Center Security, Weather Prediction, Data Preparation, Data Provenance, Climate Modeling, Computer Vision, Scheduling Strategies, Distributed Computing, Message Passing, Code Performance, Job Scheduling, Parallel Computing, Performance Communication, Virtual Reality, Data Augmentation, Optimization Algorithms, Neural Networks, Data Parallelism, Batch Processing, Data Visualization, Data Privacy, Workflow Management, Grid Computing, Data Wrangling, AI Computing, Data Lineage, Code Repository, Quantum Chemistry, Data Caching, Materials Science, Enterprise Architecture Performance, Data Schema, Parallel Processing, Real Time Computing, Performance Bottlenecks, High Performance Computing, Numerical Analysis, Data Distribution, Data Streaming, Vector Processing, Clock Frequency, Cloud Computing, Data Locality, Python Parallel, Data Sharding, Graphics Rendering, Data Recovery, Data Security, Systems Architecture, Data Pipelining, High Level Languages, Data Decomposition, Data Quality, Performance Management, leadership scalability, Memory Hierarchy, Data Formats, Caching Strategies, Data Auditing, Data Extrapolation, User Resistance, Data Replication, Data Partitioning, Software Applications, Cost Analysis Tool, System Performance Analysis, Lease Administration, Hybrid Cloud Computing, Data Prefetching, Peak Demand, Fluid Dynamics, High Performance, Risk Analysis, Data Archiving, Network Latency, Data Governance, Task Parallelism, Data Encryption, Edge Computing, Framework Resources, High Performance Work Teams, Fog Computing, Data Intensive Computing, Computational Fluid Dynamics, Data Interpolation, High Speed Computing, Scientific Computing, Data Integration, Data Sampling, Data Exploration, Hackathon, Data Mining, Deep Learning, Quantum AI, Hybrid Computing, Augmented Reality, Increasing Productivity, Engineering Simulation, Data Warehousing, Data Fusion, Data Persistence, Video Processing, Image Processing, Data Federation, OpenShift Container, Load Balancing
Edge Computing Assessment Dataset - Utilization, Solutions, Advantages, BHAG (Big Hairy Audacious Goal):
Edge Computing
Edge computing often uses similar encryption technologies as cloud computing. At rest, data may be encrypted using symmetric or asymmetric algorithms like AES or RSA. In transit, encrypted protocols like HTTPS or TLS are often used.
1. Encryption technologies for data at rest: AES, DES, RSA,
CONTROL QUESTION: What data encryption technologies are being used at rest and in transit?
Big Hairy Audacious Goal (BHAG) for 10 years from now: In ten years, a Big Hairy Audacious Goal (BHAG) for Edge Computing could be to have achieved 100% end-to-end data encryption, utilizing advanced quantum-resistant algorithms, for both data at rest and in transit, enabling maximum security and privacy for all users and devices in a highly interconnected and heterogeneous edge computing landscape.
Advanced data encryption technologies that could be used include:
1. Quantum-resistant encryption algorithms, such as:
t* New Hope: a post-quantum key-exchange algorithm based on the Ring-LWE problem
t* Frodo: a post-quantum public-key encryption scheme based on the Learning-with-Errors problem
t* NTRU: a public-key cryptosystem based on the shortest vector problem in a lattice
2. Homomorphic Encryption: a technique that allows computations to be performed on encrypted data without decryption, thus preserving confidentiality and integrity of the data.
3. Secure Multi-party Computation (SMPC): a method that enables multiple parties to jointly compute a function over their inputs while keeping those inputs private.
4. Confidential Computing: a technology that isolates and encrypts code and data in use, ensuring data confidentiality and integrity even when running in untrusted environments.
5. Trusted Execution Environments (TEEs): secure areas within a processor that provide an isolated environment for code and data, protecting them from unauthorized access or modification.
Achieving this BHAG would require significant advancements in cryptographic research, as well as widespread adoption and standardization of these advanced encryption technologies within the edge computing ecosystem. It would also demand close collaboration among industry, academia, and government organizations to ensure interoperability, security, and privacy for all stakeholders.
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"I used this dataset to personalize my e-commerce website, and the results have been fantastic! Conversion rates have skyrocketed, and customer satisfaction is through the roof."
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Edge Computing Case Study/Use Case example - How to use:
Title: Securing Edge Computing: An In-depth Case Study on Data Encryption TechnologiesSynopsis:
The rapid growth of Internet of Things (IoT) devices and the need for real-time data processing have driven the adoption of edge computing. This case study focuses on a manufacturing client who aimed to secure their data in transit and at rest in their edge computing environment. The consulting methodology involved the implementation of data encryption technologies, employee training, and policy development.
Consulting Methodology:
The consulting process began with a thorough assessment of the client′s current edge computing infrastructure. The assessment included an analysis of the existing data encryption technologies, employee training, and data management policies.
Next, the consulting team developed a customized solution that incorporated data encryption technologies for data at rest and in transit. The solution included the implementation of Advanced Encryption Standard (AES) 256-bit encryption for data at rest and Transport Layer Security (TLS) 1.3 for data in transit.
The consulting team also provided employee training on best practices for data management and security. The training included topics such as password management, phishing prevention, and incident response.
Finally, the consulting team worked with the client to develop a comprehensive data management policy that outlined roles and responsibilities for data security, incident response procedures, and data backup and recovery plans.
Deliverables:
The deliverables for this project included:
1. Data encryption technologies for data at rest and in transit
2. Employee training materials and resources
3. Data management policy template
4. Implementation plan and timeline
Implementation Challenges:
The implementation of data encryption technologies posed several challenges. One challenge was the integration of the encryption technologies with existing systems and devices. The consulting team worked closely with the client′s IT team to ensure seamless integration.
Another challenge was the training of employees on the new data management policies and procedures. The consulting team addressed this challenge by providing comprehensive training materials and resources and conducting regular follow-up sessions to ensure employee understanding and compliance.
KPIs:
The key performance indicators (KPIs) for this project included:
1. Reduction in data breaches
2. Increase in employee compliance with data management policies
3. Decrease in incident response time
4. Improvement in data backup and recovery processes
Market Considerations:
The edge computing market is expected to grow at a compound annual growth rate (CAGR) of 37.4% from 2020 to 2027 (Source: Fortune Business Insights, 2021). As the market continues to grow, it is crucial for organizations to prioritize data security in their edge computing environments.
Citations:
1. Fortune Business Insights. (2021). Edge Computing Market Size, Share u0026 COVID-19 Impact Analysis, By Component (Hardware, Software, and Services), By End-use Industry (Manufacturing, Energy u0026 Utilities, Transportation u0026 Logistics, Healthcare, and Others), and Regional Forecast, 2020-2027.
2. IBM. (2021). Advanced Encryption Standard (AES). Retrieved from u003chttps://www.ibm.com/support/pages/what-advanced-encryption-standard-aesu003e
3. Mozilla. (2021). TLS 1.3. Retrieved from u003chttps://wiki.mozilla.org/Security/Features/TLS1.3u003e
In conclusion, this case study demonstrates the importance of data encryption technologies in securing edge computing environments. By implementing AES 256-bit encryption for data at rest and TLS 1.3 for data in transit, the manufacturing client was able to enhance their data security and protect sensitive information. Additionally, employee training and comprehensive data management policies further strengthened the organization′s overall data security posture.
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