Big Data

Opaque raises $9.5M for encrypted data analytics

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Opaque, a startup that helps organizations analyze encrypted data in the cloud, today announced that it closed a $9.5 million seed funding round led by Intel Capital with contributions from Race Capital, The House Fund, and FactoryHQ. Cofounder Raluca Ada Popa says that the funds will help to expand Opaque’s ongoing contributions to the open source and data security communities.

A majority of data sits in private hands. By 2025, it’s predicted that 80% of data worldwide will reside in enterprises, and the global data economy is pegged at $3 trillion. Moreover, Gartner predicts that by 2025, 50% of large organizations will adopt privacy-enhancing data computation techniques.

Opaque, whose solution integrates with popular data and machine learning libraries like Apache Spark and XGBoost, allows users to share and analyze sensitive data without widely exposing that data. The platform leverages a combination of cryptographic fortification and secure hardware enclaves, or locked-down hardware in a processor that safeguards data from attack and attempted access outside of a trusted execution environment.

Opaque builds on the open source MC2 (Multiparty Collaboration and Competition) Project, which emerged several years ago from the University of California, Berkely’s RISE Lab. As Popa explained, the codebase contains an assortment of components that lets data owners perform analytics and train machine learning models on their data without revealing individual data to each other. Alongside cofounder Ion Stoica, the creator of Apache Spark and founder of Databricks, Popa first proposed Opaque’s framework in a whitepaper released in 2017.

“Vast amounts of confidential data is locked down at many organizations due to privacy concerns, and with it, tremendous value is lost,” Popa added. “Our vision is to set free the value of this data by providing privacy-enhancing analytics and machine learning on confidential data regardless of where it is coming from. Opaque will have a profound impact on society — banks can collaborate to fight money laundering or human trafficking, hospitals can collaborate towards better disease diagnosis and treatments, all while keeping their confidential data not visible to each other.”


Opaque’s cloud-based implementation of MC2 enables companies to migrate encrypted data and models to the cloud. Data is never exposed unencrypted on the cloud, and organizations can apply analytics or machine learning directly to the encrypted data.

Using Opaque, data owners across different teams within an organization — or across different organizations — can jointly analyze their collective data. Each data owner retains control over how their data is used, allowing them to process structured data using technologies like Spark SQL.

Enthusiasm for data encryption has given rise to an industry estimated to be worth $268.3 million by 2027. Duality Technologies pitches its homomorphic encryption platform as a privacy-preserving solution for “numerous” enterprises, particularly those in regulated industries. In March 2019, Paris-based Cosmian raised €1.4 million (about $1.5 million) for a data encryption product that combines functional and homomorphic encryption. There’s also TripleBlind and Enveil, which is developing “enterprise-scale” data encryption solutions, and security-conscious collaboration platform Cape Privacy.

But San Francisco, California-based Opaque says that Intel has collaborated with it since its inception at the RISE Lab, resulting in new data analytics, data training, and aggregation capabilities . Moreover, the startup claims that its technology has already been used by companies including Scotiabank, IBM, Ant Group, and Ericsson.

“We’ve had a very successful collaboration with the Opaque team. By enabling secure data sharing between multiple banks, Opaque’s technology has the potential to tackle several challenges, including credit risk assessment and detecting financial crime,” Ant Group chief scientist Benyu Zhang said in a statement.


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