Cryptography Enabled Data Science: Meeting the Demands of Data Driven Enterprises

Cryptography and data science can collaborate to enhance business value by enabling secure data analysis through privacy-enhancing technologies (PETs), as discussed by Duality's Dr. Marcelo Blatt and Ronen Cohen. Duality, a PET company, facilitates privacy-protected collaboration by combining data science with security to handle sensitive datasets without compromising data privacy. A case study in healthcare and life sciences, conducted by Dr. Alexander Gusev, demonstrates how homomorphic encryption can securely link and analyze encrypted datasets from multiple providers without decryption, significantly accelerating research and maintaining patient confidentiality. Homomorphic encryption, based on lattice-based cryptography, allows computations on encrypted data, preserving results as if the data were decrypted, which is particularly advantageous for machine learning applications. By reworking algorithms like decision trees into polynomials, they can efficiently operate on encrypted data, showcasing a shift in viewing encryption as a business enabler rather than merely a security measure. This approach not only enhances privacy but also unlocks data's potential across various industries.

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