Confidential Computing Hardware Based Memory Protection /
Confidential Computing Hardware Based Memory Protection / [electronic resource] :
edited by Vicente Garcia Diaz, Gloria Jeanette Rincón Aponte.
- 1st ed. 2022.
- IX, 215 p. 159 illus., 119 illus. in color. online resource.
- Advanced Technologies and Societal Change, 2191-6861 .
- Advanced Technologies and Societal Change, .
1. Design And Implementation Of Mobile Jammer For High Security System -- 2. Dual Security Based Attendence System By Using Face Recognition And Rfid With Gsm -- 3. Disasteranalysison Government Data -- 4. Edf: An Enhancement Of Droid Fusion Framework For Mitigation Of Multi Class Malware -- 5. Early Prediction Of Chronic Kidney Disease Using Predictive Analytics -- 6. Monitoring Suspicious Discussion On Online Forum -- 7. Ergonomicallydesignedsystem Forlicenseplate Recognition Usingimage Processingtechnique -- 8. Blockchain Based Privacy Securing G-Cloud Framework For E-Healthcare Service -- 9. Development Of Raspberry Pibot Surveillance Security System -- 10. Image Security Based On Rotational Visual Cryptography -- 11. Development Of Safety Monitoring For An Iot-Enabled Smart Environment -- 12. Deep Transfer Learning For Detecting Cyber Attacks -- 13. Data Security In Cloud With Hybrid Homomorphic Encryption Technique Using Gm Rsa Algorithm -- 14. Pragmatic Reform To Ameliorate Insider Data Theft Detection -- 15. Automatic Vehicle Alert And Accident Detection System Based On Cloud Using Iot -- 16. A Novel Architecture For Detecting And Preventing Network Intrusions -- 17. Cyber Hacking Breaches For Demonstrating And Forecasting -- 18. Enhancedsecurity With Crystographyusing Aes And Lsb -- 19. A Smart Security Systems Using National Instruments Myrio -- 20. Severity And Risk Predictions Of Diabetes On Covid-19 Using Machine Learning Techniques -- 21. Detection Of Cyber Threats In application platforms.
This book highlights the three pillars of data security, viz protecting data at rest, in transit, and in use. Protecting data at rest means using methods such as encryption or tokenization so that even if data is copied from a server or database, a thief cannot access the information. Protecting data in transit means making sure unauthorized parties cannot see information as it moves between servers and applications. There are well-established ways to provide both kinds of protection. Protecting data while in use, though, is especially tough because applications need to have data in the clear—not encrypted or otherwise protected—in order to compute. But that means malware can dump the contents of memory to steal information. It does not really matter if the data was encrypted on a server’s hard drive if it is stolen while exposed in memory. As computing moves to span multiple environments—from on-premise to public cloud to edge—organizations need protection controls that help safeguard sensitive IP and workload data wherever the data resides. Many organizations have declined to migrate some of their most sensitive applications to the cloud because of concerns about potential data exposure. Confidential computing makes it possible for different organizations to combine data sets for analysis without accessing each other’s data.
9789811930454
10.1007/978-981-19-3045-4 doi
Computational intelligence.
Cloud Computing.
Quantum computers.
Sustainability.
Data protection.
Computational Intelligence.
Cloud Computing.
Quantum Computing.
Sustainability.
Data and Information Security.
Q342
006.3
1. Design And Implementation Of Mobile Jammer For High Security System -- 2. Dual Security Based Attendence System By Using Face Recognition And Rfid With Gsm -- 3. Disasteranalysison Government Data -- 4. Edf: An Enhancement Of Droid Fusion Framework For Mitigation Of Multi Class Malware -- 5. Early Prediction Of Chronic Kidney Disease Using Predictive Analytics -- 6. Monitoring Suspicious Discussion On Online Forum -- 7. Ergonomicallydesignedsystem Forlicenseplate Recognition Usingimage Processingtechnique -- 8. Blockchain Based Privacy Securing G-Cloud Framework For E-Healthcare Service -- 9. Development Of Raspberry Pibot Surveillance Security System -- 10. Image Security Based On Rotational Visual Cryptography -- 11. Development Of Safety Monitoring For An Iot-Enabled Smart Environment -- 12. Deep Transfer Learning For Detecting Cyber Attacks -- 13. Data Security In Cloud With Hybrid Homomorphic Encryption Technique Using Gm Rsa Algorithm -- 14. Pragmatic Reform To Ameliorate Insider Data Theft Detection -- 15. Automatic Vehicle Alert And Accident Detection System Based On Cloud Using Iot -- 16. A Novel Architecture For Detecting And Preventing Network Intrusions -- 17. Cyber Hacking Breaches For Demonstrating And Forecasting -- 18. Enhancedsecurity With Crystographyusing Aes And Lsb -- 19. A Smart Security Systems Using National Instruments Myrio -- 20. Severity And Risk Predictions Of Diabetes On Covid-19 Using Machine Learning Techniques -- 21. Detection Of Cyber Threats In application platforms.
This book highlights the three pillars of data security, viz protecting data at rest, in transit, and in use. Protecting data at rest means using methods such as encryption or tokenization so that even if data is copied from a server or database, a thief cannot access the information. Protecting data in transit means making sure unauthorized parties cannot see information as it moves between servers and applications. There are well-established ways to provide both kinds of protection. Protecting data while in use, though, is especially tough because applications need to have data in the clear—not encrypted or otherwise protected—in order to compute. But that means malware can dump the contents of memory to steal information. It does not really matter if the data was encrypted on a server’s hard drive if it is stolen while exposed in memory. As computing moves to span multiple environments—from on-premise to public cloud to edge—organizations need protection controls that help safeguard sensitive IP and workload data wherever the data resides. Many organizations have declined to migrate some of their most sensitive applications to the cloud because of concerns about potential data exposure. Confidential computing makes it possible for different organizations to combine data sets for analysis without accessing each other’s data.
9789811930454
10.1007/978-981-19-3045-4 doi
Computational intelligence.
Cloud Computing.
Quantum computers.
Sustainability.
Data protection.
Computational Intelligence.
Cloud Computing.
Quantum Computing.
Sustainability.
Data and Information Security.
Q342
006.3