We are thrilled to announce today a strategic partnership with In-Q-Tel (IQT), a not-for-profit organization that identifies and accelerates the development and delivery of cutting-edge technologies to enhance and advance national security for the U.S and its allies. This strategic partnership with IQT will allow faster expansion and adoption within the intelligence community by helping them advance their machine learning and data analytics capabilities through privacy-enhancing technologies.
Machine learning (ML) is increasingly important in a wide range of applications, including market forecasting, service personalization, voice and facial recognition, autonomous driving, health diagnostics, education, and security analytics. Because ML touches so many aspects of our lives, it’s of vital concern that ML systems protect the privacy of the data used to train them, the confidential queries submitted to them, and the confidential predictions they return. Privacy protection — and the protection of organizations’ intellectual property — motivates the study of privacy-preserving machine learning(PPML). In essence, the goal of PPML is to perform machine learning in a manner that does not reveal any unnecessary information about training data sets, queries, and predictions. This article shows how to address privacy challenges and use PPML in XGBoost training and prediction.
AWS users occasionally need to perform analysis on data sources containing private or sensitive inputs. Inpher’s XOR Secret Computing Platform, available in AWS Marketplace, enables data scientists to train and run machine learning models while maintaining data privacy and without trading utility. As a result, data analysis and machine learning performed by XOR can improve model performance with mathematically guaranteed data privacy while ensuring the data never leaves the data source. In this post, we show you how to use XOR Trial Beta to predict the risk of coronary heart disease by performing Secret Computing. In addition, we show how to use secure multi-party computation on three distributed datasets and how to add features to the training data.
Organizations today face unprecedented challenges in leveraging the data they own: access to sensitive data needs to be controlled more efficiently; authentication methods need to be of utmost reliability; exchange of information needs guaranteed security and privacy. While these challenges are business-critical, solutions do exist today. However, there is a need to connect startups offering solutions with the organizations facing these problems in a collaborative manner. Tech4Trust is a Swiss accelerator program funded by Canton of Vaud and Canton of Geneva to address these challenges. After six months of intensive work and selecting 27 startups for the program from 50 applications, Inpher won the 1st prize for its revolutionary work in Privacy-Preserving Machine Learning (PPML).
Advances in biomedical analytics and AI have revolutionized modern healthcare. Predictive systems in this field allow for better medical and epidemiological research, as well as assist in tailoring proactive healthcare plans that can save lives. But the rise of automation in healthcare research and treatment comes with the challenge of maintaining patient privacy. As a team dedicated to responsible innovation, Inpher is pioneering the state of the art cryptographic techniques that can secure and protect privacy for patient data used in biomedical AI development -- all without compromising performance and accuracy. Inpher’s Secret Computing technology wins the iDASH Secure Genome Analysis Competition for two years consecutively.
Moore4Medical is a project pioneered by Philips, leading healthcare providers and hospitals in Europe. Inpher is one of the four Swiss companies selected by this consortium, chartered to accelerate innovation with personalized medicine and patient care with privacy-preserving AI.
As the prospect of antitrust charges against Big Tech looms larger, regulators should champion both competition and privacy as intricately connected values in the marketplace of information. In a column published on Just Security this month, Inpher’s Senior Privacy Counsel and Head of Policy, Sunny Seon Kang, challenged the proprietary concentration of data in Big Tech that has too long been weaponized as absolute bargaining power.
SOFIA, BULGARIA - Inpher Cofounder & CTO Dr. Dimitar Jetchev was recently featured on the frontpage of Forbes Bulgaria. Dimitar spoke about his recent COVID campaign for medical supplies to hospitals in Bulgaria, his upbringing, and the Secret Computing work he pioneers at Inpher.
LONDON, UK — As part of the technology working group on ‘The Role of Privacy Preserving Data Analytics in the Detection and Prevention of Financial Crime,’ Inpher is delighted to announce the publication of Future of Financial Intelligence Sharing (FFIS)’s white paper on the innovation of cryptographic privacy-preserving techniques in financial services.
Our Director of Security Innovation, Dr. Mariya Georgieva, was invited to present on fully homomorphic encryption (FHE) at this year’s ETSI Security Week. Mariya’s talk was a high-level overview on FHE primitives and highlighted the potential for real-world applications, specifically in financial services and healthcare.
Our CEO, Jordan Brandt, recently joined Jeffrey Hayzlett on C-Suite Radio to discuss how data privacy and AI are more relevant than ever in the current business climate. They discussed enterprise data privacy and security priorities, the future of private computing, and the homemade furniture that Jordan makes for his wife! Check out this link for the full podcast and see below for some of the interview’s highlights.
Inpher named in the 2020 Gartner Emerging Technologies: Homomorphic Encryption for Data Sharing With Privacy ReportMay 26, 2020
Inpher was recognized in an April 2020 report titled Emerging Technologies: Homomorphic Encryption for Data Sharing With Privacy, written by Gartner Analyst Mark Driver. Our company was acknowledged as an Example Vendor in the report, and the report mentioned TFHE in the list of HE Open-Source Projects. TFHE is the world's fastest open-source fully homomorphic encryption library — and it was built in part by Inpher’s own Nicolas Gama, Mariya Georgieva, and Sergiu Carpov. We are honored to be recognized by Gartner in this report!
As a result of the confusion and vulnerability that has arisen in the wake of the COVID-19 outbreak, there have been a surge in fraudsters looking to take advantage of individuals and businesses of all sizes. Clamping down on this can be problematic, as to truly make a dent in the increase, financial institutions would need to share the patterns of fraudulent actors that they are independently observing with other financial institutions. This can involve sensitive data inputs, like consumer IDs or credit card numbers.
Inpher Advises Senate Commerce Committee: Privacy-Enhancing Technologies Should Guide Data-Driven COVID-19 ResponseApril 9, 2020
In advance of the Congressional hearing on ‘Enlisting Big Data in the Fight Against Coronavirus’ held April 9, 2020, Inpher submitted a statement to the U.S. Senate Commerce Committee to highlight the critical need for cryptographic privacy-enhancing technologies for accountable data-driven measures against COVID-19.
Inpher Cofounder & CTO Dr. Dimitar Jetchev presented to an audience of 700 at the Real World Crypto (RWC) Symposium in New York City. Dr. Jetchev explained Inpher’s award-winning anti-money laundering (AML) solution that the team pioneered at the UK Financial Conduct Authority’s (FCA) Global Financial Crime TechSprint in August 2019.
BLOOMINGTON, IN - Inpher —in collaboration with CEA and KU Leuven— won 1st place at the 2019 iDash Competition Genome Privacy & Security Competition, hosted by the Indiana University Luddy School of Informatics, Computing, and Engineering. Inpher Chief Computer Scientist Dr. Nicolas Gama and Director of Security Innovation Dr. Mariya Georgieva led the team to first prize in the competition’s Track 2, which focused on secure genotype imputation using fully homomorphic encryption.
Inpher CEO Dr. Jordan Brandt testifies before the U.S. House Financial Services Committee on “AI and the Evolution of Cloud Computing”October 22, 2019
WASHINGTON, D.C. - On October 18, 2019, Inpher CEO Dr. Jordan Brandt testified before the Task Force on Artificial Intelligence of the U.S. House Financial Services Committee at a hearing entitled “AI and the Evolution of Cloud Computing: Evaluating How Financial Data is Stored, Protected, and Maintained by Cloud Providers.”
Inpher was recognized in a September 2019 report by Gartner Analysts Bart Willemsen, Jie Zhang, and Nader Henein. Our Secret Computing® technology was also acknowledged in the report. Thank you to Gartner for this recognition!
Last week, Inpher took part in the Financial Conduct Authority (FCA) 2019 Global AML and Financial Crime TechSprint held in London. The purpose of the TechSprint was to determine how privacy-enhancing technologies (PETs) can effectively combat financial crime, detect fraudulent activities, and prevent money laundering within the financial services industry.
Inpher was selected among a field of hundreds of applicants to participate in The FinTech Innovation Lab, a program run by The Partnership Fund for New York City and Accenture. The Lab is a “highly competitive, 12-week program that helps early- to growth-stage enterprise technology companies refine and test their value proposition with the support of the world’s leading financial service firms.”
Privacy-preserving computations on genomic data, and more generally on medical data, is a critical path technology for innovative, life-saving research to positively and equally impact the global population. It enables medical research algorithms to be securely deployed in the cloud because operations on encrypted genomic databases are conducted without revealing any individual
While citing executives from Microsoft Azure and Box, who acknowledge that "security trumps everything else", she insightfully recognizes that there is a novel approach to this dilemma that leverages encryption in-use. "New York-based startup Inpher Inc., for example, has developed technology that enables data to be processed while it remains encrypted, allowing machine learning and analytics.
When anticipating the needs of high-tech fleets distributed around the world, more data is better. The challenge is that the owners and operators of the equipment cannot share info because it is often confidential, proprietary or both. In order to provide their customers with comprehensive, accurate and cryptographically secure predictive maintenance, Thales has signed an agreement with Inpher.
"With the GDPR deadline bearing down on European financial services, compelling firms to show they comply with the rules by May 25, companies with international footprints are being rudely awoken by the potentially explosive problem of where the data lives — and who regulates it."
The Deep Learning in Finance Summit hosted by Re-Work on March 15-16 in London brings together experts and practitioners in AI from around the world to discuss novel methods and applications in FSI. Topics include fraud detection, sentiment analysis, representation learning and of course data privacy under new regulatory environments.
This year in Curaçao, the Twenty-Second International Conference for Financial Cryptography will convene with salient topics in secure computation, blockchain, cryptocurrencies and data privacy. The Inpher R&D team and academic collaborators will present their paper on High-Precision Privacy-Preserving Real-Valued Function Evaluation in the Privacy and Data Processing track.
Join global thought leaders and the Inpher executive team to discuss the future of Machine Learning in Financial Services at Stanford University on February 8. The AI in Fintech Forum hosted by Kay Giesecke includes presentations and insights from the following experts:Senator Mark Warner, Andrew Rachleff, CEO of Wealthfront and Cofounder of Benchmark Capital, Dr. Peter Cotton
Alexander Petric presents "Touch But Don't See; Applications of Encrypted Search and Computing" at Trustech in Cannes.
Inpher will join the Thales Cybersecurity program at the famed Station F to implement next generation products for analytics and machine learning on encrypted data.
As a finalist of the UBS Future of Finance Challenge, Inpher will present the capabilities of Secret Computing™ to address myriad challenges facing Investment Banking, including: Facilitation of data science knowledge sharing and insight, Machine Learning in capital markets, and Regulatory compliance, particularly with MiFID, GDPR and PSD2
June 16, Paris France. Out of over 40 applicants, Inpher was selected as a finalist for the Banking Cybersecurity Innovation Award by Société Générale and Wavestone. Secret Computing™ technology not only helps the bank protect their data, but empowers their data sciences teams to analyze it while remaining compliant with the upcoming General Data Privacy Regulation.
The Wall Street Journal reported on ING's use of Inpher's XOR Secret Computing™ Engine to run analytics on sensitive databases in multiple jurisdictions across the EU. This enables compliant and privacy-preserving machine learning to meet current and upcoming regulations such as GDPR, while opening the opportunity for secure secure cloud computing.
As one of the largest financial services conferences, the Temenos Community Forum (TCF) brings together representatives from across the financial services community including Temenos customers, product experts and thought leaders from around the globe. The theme this year is Real World Fintech.
Inpher was selected as one of 10 international startups for ING's Fintech Village to build a Proof of Concept with our next generation product for zero-knowledge computing. This will help ING to securely scale their cloud initiative and improve their analytical models by enabling computation on private data sources without ever seeing the data.
The finalists for the Swiss Fintech Awards have been announced. We are honored to be in the company of esteemed colleagues: AAAccell, Gatechain, Crowdhouse and Advanon.
From Ellipticnews, "The best paper award went to Ilaria Chillotti, Nicolas Gama, Mariya Georgieva and Malika Izabachène for “Faster Fully Homomorphic Encryption: Bootstrapping in less than 0.1 Seconds”, which shows that homomorphic encryption (in this case the GSW scheme with packed ciphertexts, together with a bunch of clever new ideas) is gradually becoming closer to practicality.
Real-time encrypted search is the ultimate defense against prying eyes and a reassuring tool for banking security experts. Inpher have provided an enterprise-grade development platform for encrypting and interrogating terabytes of data across thousands of users, so you can be sure your sensitive search data will stay private.
Inpher's CEO was interviewed as a selected startup at Sibos 2017
"Jordan Brandt from Inpher highlighted that the privacy issue is tightly coupled, though not synonymous, with trust. Jordan added that ‘now we have very standard implementations of SSL and cryptographic protocols that enable us to establish trust between the buyer and the seller. Obviously e-commerce is now firmly established and we’re not going back.
Twenty-four Swiss FinTech start-ups will present their innovative solutions to the global financial community next week at the “Swiss FinTech Corner” at the Sibos convention in Geneva. Arranged through a public-private partnership, the booth provided by the event organizer aims to promote the excellence of innovative financial technology in Switzerland.
Dimitar Jetchev, CTO d’inpher.io, une jeune pousse basée entre San Francisco et l’EPFL.
Inpher has launched a software development kit that encrypts data at its inception, while supporting search and basic analytical functions without decryption. Industry applications range from banking, insurance and healthcare to platforms for managing Internet of Things (IoT) devices.