Defined as Post Quantum Cryptography, FHE is also safe from Quantum Computing attack and can be used in areas such as: fraud detection, Anti-Money Laundering, Predictive Analytics, and Loan Scoring all without data disclosure.

With data security, you need to limit access to the day-to-day transaction stream to minimize the risk of data disclosure and breach and with fraud detection. You want to consolidate and review transaction data at the highest level of business to detect and act on fraud signatures. 

Real-time FHE allows both goals to be achieved by having the data remain encrypted throughout its processing lifecycle. There need not be restrictions on data access. By also encrypting fraud signatures, fraud detection is operating on encrypted data is itself, not breached to tip off the bad actors.

Current security techniques coupled with HIPAA, GDPR, and other patient privacy regulations silo EHR data and restrict the application of AI, Big Data, and Analytics across the multiple organizations required to extract those insights from large patient populations.

Maintaining EHR data in FHE encrypted form enables unrestricted AI, Big Data, and Analytics to be applied to multi-organization data sets while ensuring patient confidentiality is maintained and data is secure from unauthorized disclosure.

For companies selling those services (testing, business analytics, health and wellness assessments, design, accounting, …), a key business requirement is having customers trust the stewardship of their confidential and proprietary data while its being processed. FHE removes that requirement while guaranteeing that both the data and the result remain confidential.

Each year, large Financial Institutions spend hundreds of millions completing KYC (know your customer) checks on new and existing customers, however, no framework exists to collectively mitigate risk through the sharing of customer risk intelligence across privacy jurisdictions or between entities. This lack of access to existing intel often forces institutions to make risk-rating decisions based on incomplete information.

Utilizing FHE, a flexible and adaptable trust framework capable of facilitating secure and private KYC and Customer Due Diligence (CDD) processes to enhance intelligence-led decision making. Analysts are able to securely and privately cross-match and search regulated data across privacy jurisdictions in a business-relevant timeframe while ensuring sensitive assets remained protected during processing in accordance with regulatory requirements.

Share/monetize data for better analytics such as drug trials with third parties while keeping the underlying patient and partners data confidential.