Mathematical Certainty in Data Security
By Jorge Myszne, Niobium
EEtimes Europe (November 28, 2024)
The evolution of the digital landscape has been nothing short of transformative. From the initial Compute Era, which laid the groundwork for our modern infrastructure, to the Data Era of the 2010s, where the value of data became increasingly evident, we have now entered the Intelligence Era. In this phase, data—particularly sensitive data—is the primary driver of innovation, automation and decision-making. However, the rise of sensitive data has also raised pressing challenges: How can we harness the power of this data without compromising its security or privacy?
In this article, we explore the shortcomings of traditional data security methods and the potential of fully homomorphic encryption (FHE) to provide mathematically guaranteed confidentiality and system immunity for critical applications. For engineers and developers focused on creating responsible computing solutions, this emerging technology may hold the key to achieving both the data analysis capabilities and the privacy protections needed for next-generation systems.
To read the full article, click here
Related Semiconductor IP
- Embedded Hardware Security Module for Automotive and Advanced Applications
- Hardware Security Module
- ARC SEM120D Security Processor with DSP for Low Power Embedded Applications
- ARC SEM110 Security Processor for Low Power Embedded Applications
- ARC SEM130FS Safety and Security Processor
Related White Papers
- AES 256 algorithm towards Data Security in Edge Computing Environment
- Secure Your Security Key in On-Chip SRAM: Techniques to avoid Data Remanance Attacks
- Designing for safety and security in a connected system
- Enhancing privacy and security in the smart meter lifecycle
Latest White Papers
- Reimagining AI Infrastructure: The Power of Converged Back-end Networks
- 40G UCIe IP Advantages for AI Applications
- Recent progress in spin-orbit torque magnetic random-access memory
- What is JESD204C? A quick glance at the standard
- Open-Source Design of Heterogeneous SoCs for AI Acceleration: the PULP Platform Experience