The Census Bureau’s Dual Mandate The U.S. Census Bureau operates with a dual mission: 1. To conduct and disseminate the decennial census as mandated by Article 1, Section 2 of the U.S. Constitution. 2. To ensure confidentiality of respondent data, as reinforced by Title 13 of the
Self Sovereign Identity
The shift from the tumultuous world of cryptocurrencies to the structured implementation of self sovereign identity exemplifies how the technology's potential can be harnessed responsibly.
Federated Analytics
Differential Privacy (DP) is a mathematical framework designed to provide robust privacy guarantees when releasing statistics from datasets. It ensures that the inclusion or exclusion of any single individual's data has a negligible effect on the outcome of an analysis. This article serves as an in-depth explanation of
Artificial Intelligence (AI) agents are revolutionizing various industries by automating tasks, enhancing decision-making, and improving efficiency. These agents operate autonomously or semi-autonomously, perceiving their environment, processing information, and taking actions to achieve specific goals. Understanding the different levels of AI agents is crucial for effectively leveraging their capabilities. Level 1:
The shift from the tumultuous world of cryptocurrencies to the structured implementation of self sovereign identity exemplifies how the technology's potential can be harnessed responsibly.
Shielding sensitive information: Leveraging differential privacy to counteract database reconstruction attacks and preserve individual confidentiality in aggregate data sharing.
Artificial intelligence (AI) has made significant strides across various industries, yet the challenge of accessing large, diverse datasets—particularly in sectors like healthcare—persists due to privacy concerns. Federated learning emerges as a transformative solution, enabling collaborative model training without compromising data security. What Is Federated Learning? Federated Learning (FL)
The Challenge: Evolving Cybersecurity Threats Digital transformation continues to improve our lives as new digital solutions save people time and money in day-to-day interactions. However, with an increased dependence on digital solutions comes a greater exposure to continuously evolving cyber threats. In 2022, 7 in 10 Canadians (70%) experienced a
The Discovery Phase is where we dive deep into understanding your business processes, your users, and the environment in which you operate. This phase is about gathering the insights we need to identify opportunities and set a strong foundation for designing impactful solutions. By examining the current state of operations,
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Shielding sensitive information: Leveraging differential privacy to counteract database reconstruction attacks and preserve individual confidentiality in aggregate data sharing.
Artificial intelligence (AI) has made significant strides across various industries, yet the challenge of accessing large, diverse datasets—particularly in sectors like healthcare—persists due to privacy concerns. Federated learning emerges as a transformative solution, enabling collaborative model training without compromising data security. What Is Federated Learning? Federated Learning (FL)
The Issuance experience pattern uses digital credentials to securely and efficiently issue verifiable proofs of identity, attributes, or claims. By storing verifiable credentials in a digital wallet, issuers can provide individuals and organizations with trusted and secure digital credentials. This enhances trust and security in digital interactions. Issuance Process Issuing
Access Overview The Access experience pattern leverages digital credentials to simplify and secure the way users access services online or in-person. By utilizing digital credentials stored in a digital wallet, users can seamlessly prove their identity or attributes without relying on traditional usernames and passwords or physical IDs. This approach
With digital trust technology, we enable digital interactions that are trusted, user-friendly and secure. From simplifying access to services by no longer relying on conventional login methods (e.g. username and password combinations) to managing digital identities easily and securely in the Xanadu ID wallet. Digital trust experiences are unique
The true value of digital trust technology is realized at scale when users are able to leverage their credential for a large variety of use cases. Successful and sustainable digital trust adoption requires an ecosystem approach, where use cases are explored in context to their environment (e.g. service area,
The Challenge: Evolving Cybersecurity Threats Digital transformation continues to improve our lives as new digital solutions save people time and money in day-to-day interactions. However, with an increased dependence on digital solutions comes a greater exposure to continuously evolving cyber threats. In 2022, 7 in 10 Canadians (70%) experienced a
During the Scale Phase we take tested solutions and expand their reach across your organization or audience. This is about moving beyond the prototype, ensuring your service or product works seamlessly at scale, and setting up systems to maintain and grow its impact. Scaling isn’t just about replicating success—
In the Trial Phase, we focus on creating tangible solutions that are ready for the real world. By collaborating with your team and engaging directly with users, we ensure that every
The Discovery Phase is where we dive deep into understanding your business processes, your users, and the environment in which you operate. This phase is about gathering the insights we need to identify opportunities and set a strong foundation for designing impactful solutions. By examining the current state of operations,
At this stage, we’re not just preparing your project; we’re investing in your success. We bring our expertise to help your organization build better information infrastructure and processes that are aligned with your customers’ needs and your key performance metrics.