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The Data Dilemma: How Data Services Companies Can Unlock New Value with Privacy-Enhancing Technologies

Secure AI Collaboration for Data Service Companies

Data is the lifeblood of modern businesses, and for data services companies, it’s their core asset. This data fuels the creation of powerful machine learning (ML) and artificial intelligence (AI) models. But a significant hurdle arises: how to extract maximum value from these assets without jeopardizing their worth or violating privacy?

  • The IP Risk: Data services companies understandably hesitate to share their ML/AI models with third parties. Doing so could expose the valuable intellectual property (IP) that went into building and refining those models.
  • The Privacy and Value Dilemma: Raw data holds immense potential, but it often contains sensitive information like personally identifiable information (PII) or protected health information (PHI). This creates both a privacy responsibility and a reluctance to relinquish control of such valuable raw data.

The result is a stalemate. Data that could drive innovation and new business models / revenue streams remains untapped.

Privacy-Enhancing Technologies (PETs) to the Rescue

Privacy-enhancing technologies (PETs), also referred to as privacy-preserving analytics, offer a breakthrough. They provide mechanisms to utilize data and reap the benefits of ML/AI models without needlessly exposing the underlying sensitive data or the IP embedded in the models themselves. Here’s why data services companies must embrace PETs:

1. Unlocking New Data Revenue Streams

  • Safe Data Collaboration: PETs like homomorphic encryption or federated learning allow data analysis across multiple organizations without pooling the sensitive data itself. This fosters data-sharing ecosystems and opens new markets for data insights.
  • Protecting Model IP: AI models can be deployed within Trusted Execution Environments (TEEs) where both a model with proprietary IP and data containing sensitive information can be used safely and securely. TEEs create secure, isolated computing enclaves, ensuring models remain functional but resistant to reverse engineering, protecting their intellectual property and the raw data is never exposed.

2. Build Trust and Compliance

  • Consumer Confidence: Demonstrating a commitment to privacy builds trust, leading to greater willingness for consumers to share data in exchange for personalized services or valuable insights.
  • Meeting Regulations: PETs are a critical tool to achieve compliance with regulations like GDPR, CCPA, and industry-specific or country-specific privacy laws. This reduces the risk of fines and reputational damage.

3. Drive Data-Fueled Innovation

  • Accelerating Research: Researchers can access and analyze real-world data when protected with PETs. This will lead to faster breakthroughs in healthcare, finance, and law enforcement.
  • Creating New Business Opportunities: Protections offered by PETs enable novel opportunities. For example, data marketplaces (e.g., Databricks and Snowflake) can allow AI models to be securely deployed for use with private data within TEEs, or where sensitive data is analyzed without leaving a company’s control.

Getting Started

Data services companies need to act quickly. Investing in PETs isn’t just about compliance – it’s a competitive advantage. Start with these key considerations:

  1. Understanding Governance Requirements
  • Thoroughly assess privacy regulations and ethical guidelines that apply to your specific use cases.
  • Identify the context of data use: Ask questions such as “what specific data is involved (types and sensitivity levels)?”; “From what locations will the data originate or be accessed?” and “what computations or analyses will be performed?”

2. Matching PETs to Capabilities

  • Carefully research the strengths and limitations of different PETs (homomorphic encryption, federated learning, TEEs, etc.).
  • Select the PETs that align best with the context of your data, the model, and the desired outcomes, all while considering governance requirements.
  • Often times taking a PET platform approach provides the most flexibility and can address most use cases.

Important Note: Remember that successful PET implementation often involves a combination of technologies, legal frameworks, and a deep understanding of your unique business needs.

The future of the data-driven economy hinges on privacy. PETs enable data services companies to harness the power of their data and AI models responsibly – a win-win for businesses and consumers alike.

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