Break Down Data Silos with Cryptographic Security Using Inpher on…
Inpher’s integration with Oracle Cloud represents a significant advancement in secure and private cloud computing. This collaboration enhances data security, making it an...
The Privacy-Utility Trade Off with Generative AI
Security technology must keep pace with the development of LLMs. A way out of the privacy-utility trade off is to secure users’...
Business AI Preparedness- The Race to Leverage Secure AI
Businesses do not have to throw caution (and security and privacy) to the wind in their hurry to start benefiting from...
Balancing Privacy and Explainable AI in Semiconductor Manufacturing
Given their ability to generate clear explanations while preserving accuracy, SHAP values have large potential to support Explainable AI. By bringing machine learning...
Inpher, Oracle & Sensitive Data Workloads: Generating Business Value from…
Customers rely on Oracle to manage their most sensitive data workloads. Inpher empowers organizations to generate value by operating collaboratively on the data found in...
Balancing Data, Privacy, and Business Learnings
For large retailers like Walmart, leveraging data strategically and in a privacy preserving way can produce powerful results and even more powerful actionable...
SHAP Values In Support of Forecasting
Given their ability to generate clear explanations while preserving accuracy, SHAP values have large potential to support Explainable AI. By bringing machine learning...
The Rise of Financial Fraud in the Digital Era and…
In a world of escalating financial fraud, Explainable AI has the potential to make powerful, ML-based fraud detection a realistic option that aligns with regulations, ethics,...
Privacy-Preserving Model Explainability: What It Is & How Data Influences…
Explainable AI can help organizations align with ethical and regulatory imperatives in order to unleash the potential of their data, including private...
Governance and Privacy-Enhancing Technologies: Why Every Enterprise Needs to Adopt…
Privacy-Enhancing Technologies (PETs) represent a revolutionary capability that facilitates a delicate equilibrium between privacy and utility within information...
AI, Data and the Privacy Gap: Institutionalizing Governance within the…
AI systems have an insatiable appetite for data. Rapid advancements mean they are now capable of massive ingestion and use of almost all publicly available data–so...
Moore4Medical Accelerates Patient Monitoring for Improved Outcomes with Inpher
Moore4Medical is a European project led by Philips, comprised of a total of 65 partners, including universities, research institutes, hospitals, and private...
Privacy Budget in Support of Privacy by Design
As of late the concept of privacy by design has been formalized in data protection laws globally. Having evolved as a way to consider the broader systems and processes in...
The Seven Foundational Principles of Privacy by Design
First introduced in 1995, privacy by design evolved alongside privacy-enhancing technologies (PETs) and is intended to address the broader systems and processes in which PETs...
Privacy Budget: A Roadmap to Privacy Preserving Data Collaboration
In the world of data collaboration, it is generally understood that when two or more parties share training data for their AI models, they achieve more accurate predictions...