Digital transformation, characterized by activities such as cloud migration, adoption of cloud-native architecture, embracing the As-a-Service model, and leveraging AI-powered innovations, has significantly contributed to the exponential growth of data generation and utilization. As a result of this transformation, data has emerged as a crucial asset within the organizational and business landscape, offering invaluable insights that drive the success of strategic initiatives and decision-making processes. Unfortunately, the value of data to organizations is recognized not only by organizational leaders but also by threat actors, making data governance a complex and challenging task. To address this challenge, Gartner, a technology research and consulting company, predicted that Privacy-Enhancing Computation (PEC) will be widely adopted by 50% of large businesses by 2025. This post explains Technologies in Privacy-Enhancing Computation in enabling data governance.
Privacy-Enhancing Computation (PEC) does not have a specific or rigid definition, but it can be described as a set of technologies designed to help secure and restricted handling of sensitive data while enabling efficient data processing and management. These technologies work together to ensure privacy and confidentiality while maintaining the utility of the data.
The technologies included in PEC are:
1. Homomorphic encryption
Homomorphic encryption is a technology that applies cryptographic techniques to perform computation on encrypted data with the need for decryption. It preserves data privacy by allowing it to remain encrypted while during data processing.
2. Multi-Party Computation (MPC)
MPC enables multiple parties to collaborate on a computation while maintaining the privacy of their inputs. It ensures that each party receives only the necessary information required to complete the computation, thereby preserving privacy and confidentiality.
3. Differential Privacy
Differential privacy is a concept that adds noise or randomness to query results to protect the privacy of individual data points. It provides a mathematical framework to measure and control the privacy risks associated with data analysis.
4. Trusted Execution Environment (TEE)
A TEE is a secure and isolated environment within a computing device that protects sensitive data and computations from external threats. It gives a secure execution environment that protects data integrity and confidentiality.
5. Zero knowledge proofs (ZKP)
Zero knowledge proofs is a powerful technology in PEC that are based on complex mathematical algorithms and cryptographic techniques, such as interactive proofs, non-interactive proofs, and commitment schemes. They enable parties to interact and validate computations without revealing any sensitive data, while achieving trust and verification.
Overall, successful implementation of PEC requires a deep understanding of attack surfaces, risks, and a wide range of privacy-enhancing technologies. By prioritizing what matters most based on the organization’s needs and leveraging the right PEC technologies, along with establishing an effective data governance framework, organizations can protect sensitive data while leveraging the valuable insights it offers.
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Whether you need a customized solution for your entire organization or a point solution for a specific area of your business, E-SPIN Group has the expertise and experience to help. Contact us today to learn more about how we can assist with your organization’s needs and requirements.
Other post you may be interested in:
- The need for Privacy enhancing Technologies (PETs) in Business
- Best practices for data privacy GDPR and beyond
- AI Governance Framework for better AI adoption and Protection against AI risk
- The Outcomes of AI Governance: Addressing the Drawbacks of AI
- Emergence of Privacy-Enhancing Computation: Enhancing Data Privacy and Security