Secret Computing - Inpher
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© Secret Computing
\\ secret computing
\\ PRIVACY PRESERVING MACHINE LEARNING
with the INPHER XOR ENGINE
The world’s largest companies rely on Inpher’s XOR Secret Computing© Service to
power their privacy preserving computing for data in use encryption across teams,
countries and industries. Inpher utilizes secure multiparty computation in its core
XOR Engine allowing data analysts to run sophisticated machine learning functions
against single or multiple data sets to generate an outcome without ever seeing or
transferring the data from the underlying data sources.
PRIVATE PRECISE QUANTUM
Privacy preserving by Currently supporting RESILIENT
design with no data up to six decimal A cryptographically
leakage or exposure precision ensuring no secure way of
& growing regulatory tradeoff in accuracy training your data
compliance models
PRACTICAL NO THIRD ENTERPRISE
Existing function
PARTY Commercial grade
library, fast execution, No third-party platform deployable
effective processing processors or trusted across infrastructure
requirements intermediaries setups\\ secret computing
PRIVACY PRESERVING COMPUTING
Inpher believes that data privacy and security are fundamental to the future of data analysis and computing. That is why we
developed a product and fine-tuned a technology that allows for privacy compliant analysis on private data sources with zero
exposure to the underlying data.
More (good) data means better predictions and business outcomes. Inpher’s Secret Computing© products enable data scientists
& analysts to unlock sensitive data for their machine learning models without ever exposing or compromising the sensitive data
in the process.
Inpher’s pioneering cryptographic Secret Computing© technology powers advanced analytics and AI
applications without exposing or transferring sensitive data across departments, organizations or
jurisdictions.
Our core commercial solution, called the XOR Secret Computing© Engine, is built off our proprietary advances in secure multiparty
computation. The XOR Secret Computing© Engine is based on secret sharing and Fourier approximation of real-valued functions
that enable secure evaluation of functions across multiple private data sources. This technique allows for the maintenance of
privacy without the tradeoff of precision and allows data analysts to run functions against single or multiple data sources without
ever revealing the inputs.
The XOR Engine is fast relative to plain text computing and other privacy preserving techniques, as well as being very precise. Our
current version supports up to six decimal precision, ensuring no tradeoff in accuracy. The service is quantum resilient and our
process ensures mathematically guaranteed cryptographic security. We support a significant number of practical data science
functions for the purposes of training models directly. Additionally, there is no third party or middleman performing the analysis
with our solution.
Inpher’s Secret Computing© Engine meets the approval of legal firms and government regulators with respect to data transfer
requirements. In other words, performing data analysis with XOR in one jurisdiction can be performed on plain text data, like in
European Union countries, without necessitating the transfer of plain text or encrypted data.
What is the XOR Secret Computing© Engine?
+ Enables privacy-preserving data analysis + Enables analysis of previously inaccessible data
+ Supports analysis across multiple data sets with + Only the output is returned without viewing
different features the inputs
+ Is GDPR Data Transfer Compliant + Allows data owners to commercialize more
data
+ Has mutual privacy controls for both the data + Supports advanced machine learning
owner and analyst functions and statistical inference predictions
+ Inpher supports access through a user interface, + Provides flexible and secure implementation
API endpoints and a Python library options either on-prem or in the cloud\\ secret computing
XOR USE CASE EXAMPLES
Inpher’s XOR Engine can be implemented across an entire enterprise or as a
standalone service to meet the needs of different workflows in the
organization.
____________
Privacy Preserving Analysis Across Departments, Jurisdictions and Organizations
Inpher customers use the XOR Secret Computing™ Engine to build analytical models using their data from multiple countries with
stringent data security and personal privacy rules, like countries in the European Union. Proprietary algorithms generated by data
science teams are compiled with XOR and secretly computed by all regional datacenters and/or cloud services providers without
revealing any sensitive information; so ultimately no PII is exported from any jurisdiction. With more data sources, the teams can
build improved models and more accurately predict anything from loan defaults and payment fraud to identifying which products
or services a customer is likely to buy.
____________
Blind Trade Matching
Banks, broker/dealers and hedge funds all have the problem of information leakage when revealing positions that they want to
buy, sell, borrow or loan. Using XOR, trading partners can identify matches between multiple parties without their sensitive
position sizing leaving their data center and only exposing minimum values to prevent an information advantage by a
competitive counterparty.
____________
Fraud Risk Data Collaboration
Transactional fraud risk is a concern that impacts every financial services institution. Using XOR, banks can now both obtain
more third-party data, but also collaborate with other banks securely to improve their fraud models. This process prevents the
transfer or exposing of their own fraud data and allows participating banks to improve their models in a scenario where every
participating bank wins.
____________
Predictive Maintenance
Clients with technology and components made from different and often competing manufacturers are using Inpher to identify
situations, holistically, where a component of a larger device has failed to troubleshoot the issue without revealing the specific
manufacturer.\\ secret computing
ABOUT US
A FEW MEMBERS OF OUR INTERNATIONALLY RECOGNIZED TEAM OF EXPERTS
Inpher is a team of award winning and veteran founders,
cryptographers and software engineers who are
internationally recognized as experts in their fields. Every
day, we are turning ideas that were only theoretical just a
few years ago into commercial production for the benefit
of our clients.
Inpher is led by co-founders Dr. Jordan Brandt, CEO, and
Dr. Dimitar Jetchev, CTO. The company has 26 employees
of which 9 have Ph.D.’s in related disciplines. We have
published over 300 academic and peer reviewed papers
and our colleagues are considered internationally
recognized thought leaders in the theory, development,
legal implementation and future of privacy preserving
computation.
We have offices in New York, San Francisco and Lausanne,
Switzerland to meet the needs of clients currently
distributed in the United States, Canada, Europe, China &
Southeast Asia.
We have completed a Series A fundraising round in late
2018 lead by J.P. Morgan Chase & Co. with Crosslink
Capital, Bowery Capital and Alpana Ventures also
participating.
“We’re not making investments for a 5-year period. This is stuff we’re working on live now.”
Samik Chandarana, Head of Data Analytics, J. P. Morgan Chase & Co.WHAT IF
YOU COULD ACCESS MORE DATA?
WHAT IF
YOU COULD MAKE BETTER MODELS?
WHAT IF
YOU COULD DO IT SECURELY & PRIVATELY?
IT’S NOT MAGIC
IT’S INPHER SECRET COMPUTING
inpher.io
info@inpher.io
twitter : @inpher_io
36 West 25th St., Suite 300
New York, NY 10010
EPFL Innovation Park Bâtiment A
1015 Lausanne, SwitzerlandYou can also read
