As data breaches and cyber attacks become more sophisticated, traditional encryption methods face unprecedented threats.
The rise of quantum computing also poses significant risks to current encryption methods as quantum computational power may render them obsolete. Additionally, the exponential growth of machine learning and artificial intelligence is driving the need for secure computing as these technologies rely heavily on large, high-quality datasets.
If compromised data is input into an AI model, the resulting output will also be compromised, so it is important to ensure not just the quantity of data, but also its quality, integrity, and accuracy. Fully Homomorphic Encryption (FHE) offers a path to transform the way sensitive data is handled and shared.
The Data Dilemma: Protecting vs. Leveraging
It’s often said that data is our most valuable global asset. But its true value is only realized when it’s used to make informed decisions, such as improving operational efficiency, developing products, or understanding societal trends. Organizations are increasingly looking for ways to optimize this value through new technologies such as AI, ML, and data collaboration. Yet valuable data often remains siloed within organizations, and the most valuable data is usually the most sensitive data.
Data breaches by criminal organisations can have devastating consequences not only for organisations but also for the individuals whose personal data has been stolen. This data must be kept confidential and shared only with trusted parties. Yet the need for collaboration creates tension between the benefits of data sharing and the risks to confidentiality.
Encryption is typically only applied when sensitive data is in motion or at rest. To process the data, it typically must first be decrypted, exposing it to risk. This creates a dilemma: protect the data and limit its use, or exploit it and increase the risk of a breach.
FHE resolves this tension by making encrypted data computationally accessible; data can be shared without being exposed or vulnerable, making it useless to an attacker even if it is intercepted. By allowing multiple parties to manipulate data without actually having access to it, FHE ushers in a new era of secure computing and supports the new data economy.
Challenges of FHE and the Potential of Silicon Photonics
Despite its great potential, FHE faces significant challenges in its deployment, primarily due to the significant computational power required and the inefficiencies of traditional electronic processing systems. FHE requires specialized hardware and significant processing power, which leads to high energy consumption and increased costs. However, FHE enabled by silicon photonics (which uses light to transmit data) offers a solution that makes FHE more scalable and efficient.
Current electronic hardware solution systems are reaching their limits and struggling to process large amounts of data and meet the demands of FHE. However, silicon photonics can significantly improve data processing speed and efficiency, reduce energy consumption, and lead to large-scale implementation of FHE. This will unlock a variety of possibilities in areas such as AI, data collaboration, blockchain, and data privacy across various sectors including healthcare, finance, and government. This could lead to significant advances in medical research, fraud detection, and enable large-scale collaboration across industries and geographies.
The road to widespread adoption
The COVID-19 pandemic has highlighted the real-world outcomes that can occur when organizations work effectively together toward a common goal. Vaccine development, typically a lengthy process, was accelerated by collaboration among major pharmaceutical companies. For example, a partnership between BioNTech, Fosun Pharma, and Pfizer led to the rapid development of the widely distributed Pfizer-BioNTech vaccine. This involved the sharing of large amounts of unique and valuable information, including biomedical data and trial results, often without any formal agreement in the early stages. However, it also highlighted the risk of confidential information being leaked and the need for better tools to ensure data security and confidentiality.
Privacy enhancing technologies (PETs) have traditionally been complex and difficult to deploy. However, FHE stands out by maintaining full encryption security, keeping data protected from unauthorized access during processing. This allows data scientists and developers to run data analysis tools on sensitive information without seeing or compromising the sensitive data. Implementing FHE can be challenging for users without cryptography skills, but modern FHE software tools are making FHE increasingly accessible without deep cryptography knowledge. Additionally, the regulatory environment is evolving to support the widespread adoption of FHE. Guidance from agencies such as the Information Commissioner’s Office (ICO) and regulatory sandboxes in regions such as Singapore are supporting the development of FHE. Its uses are broad and span government-level data protection, cross-border financial crime prevention, defense information exchange, healthcare collaboration, and AI integration.
For example, in healthcare, FHE enables secure analysis of patient data to support advanced research while keeping patient data confidential. Financial institutions can perform secure calculations on encrypted data to enable risk assessment, fraud detection, and personalized financial services. Government and defense companies can also enhance national security with secure communications and data processing in untrusted environments. Additionally, FHE enables the secure training of machine learning models on encrypted data, combining the power of AI with data privacy.
The Future of Data Security with Silicon Photonics FHE
FHE will transform the future of secure computing and data security. By enabling computations on encrypted data, FHE provides a new level of protection for sensitive information and addresses key challenges in privacy, cloud security, regulatory compliance, and data sharing. While technical challenges remain, advances in FHE technology are paving the way for its widespread adoption.
As we continue to generate and rely on vast amounts of sensitive data to solve society’s greatest challenges, FHE enabled by silicon photonics offers a secure and efficient solution that ensures data is used and remains confidential. The future of secure computing is about organizations doing more with their data through secure sharing or processing, realizing its full potential without compromising privacy.
Nick New is CEO and founder of Optalysys.
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