Press Coverage

Inpher Press Coverage

FEATURED ARTICLES & REPORTS IN REVERSE CHRONOLOGICAL ORDER (MOST RECENT FIRST)

Euro Journal: Behind the Idea: Payhawk Privacy in a Data-Centric World

Certainly, one of my favourite examples is an organization called Inpher that permits information scientists to compute distributed information securely and privately without ever exposing it. It’s called Secret Computing® technology and allows banks to construct danger and fraud evaluations without ever disclosing their very own information. With this technology, it is possible to discover trends and determine the transfer of funds amongst tons of banks without revealing their information. We’re going to see a variety of innovations within this sector. For example, Inpher’s XOR platform uses “encryption-in-use” (a new approach that ensures data is never unprotected) to run machine learning computation across multiple parties’ data. It is used most often to protect financial, health, and other personal information.

SAP Insights: Protecting Privacy in a Data-Centric World

On the more technical side of the use case spectrum, for companies that need to feed machine learning models high volumes of training data, there are many options. For instance, Inpher’s “Secret Computing” technology aims to balance the tension between data access and data privacy. “Our customers are facing the challenges of needing to do data sharing and data collaboration, especially with machine learning workloads that require more and more data to be effective. But accessing that data is getting harder and harder with privacy restrictions and new regulations,” says Jordan Brandt, co-founder and CEO of Inpher. Its XOR platform uses “encryption in use” (a new approach that ensures data is never unprotected) to run machine learning computation across multiple parties’ data. It is used most often for financial, health, and other personal information.

PR Newswire: Inpher Secures Strategic Investment from Swisscom Ventures to Accelerate Growth in Secure, Privacy-Preserving Computing

“The strategic investment from Swisscom underlines Inpher’s continued partnership with trusted industry leaders in data security, privacy and AI.  We look forward to implementing the vanguard of Privacy-Enhancing Technologies (PETs) to ensure cryptographically guaranteed trust for the many industries we serve,” said Pär Lange, Investment Partner at Swisscom Ventures. “The patented technology, enterprise-ready product and expert team at Inpher demonstrate the next wave of innovation and scale in this rapidly expanding market.”

Gartner: Hype Cycle for Privacy 2021

“Security and risk management leaders managing technology, information and resilience risk consider privacy a top priority. This Hype Cycle describes the technologies that protect personal data and business value to build trust with individuals. Use this Hype Cycle to prioritize risk and investment.” Gartner lists both Secure Multi-Party Computation (MPC) and Homomorphic Encryption (HE) as having Transformational Benefits in this hype cycle. Inpher’s XORTM Platform is the industry’s first SaaS-based Privacy-Preserving Computing Platform that’s built on MPC. Additionally, Inpher is the leading contributor and maintainer of TFHE – an industry-leading HE library.

InfoQ: Google Open-Sources Fully Homomorphic Encryption Transpiler

Google has open-sourced a general-purpose transpiler able to convert high-level code to be used with Fully Homomorphic Encryption (FHE). Google’s transpiler has two major components. On the one hand, it uses Google’s open-source XLS SDK to leverage its compilation pipeline and convert higher-level language operations into lower-level boolean operations as required by FHE. On the other hand, it uses Google’s TFHE fully homomorphic encryption library to go from the intermediate representation provided by XLS to an HFE computation.

Heidi.News: Training Algorithms Without Revealing Personal Data (English Translated)

Reconciling the development of efficient AI algorithms with the protection of personal data is a major issue in which Switzerland has an interesting role to play. One of the key players is a start-up called Inpher. Based in the United States and Lausanne, Switzerland, it is developing an innovative technology called “Secret Computing” to preserve data confidentiality while making it possible to feed complex algorithms.

STARTUPTICKER: Tech4Trust Second Seasons Ends on a High Note

After 6 months of intensive work in the context of a global pandemic, the 2nd edition of the Swiss start-ups acceleration program Tech4Trust by Trust Valley is a success. At the closing ceremony, the 3 most promising companies were voted by the advisory committee. Inpher takes 1st place, Strong Network 2nd and DuoKey 3rd.

PR Newswire: Inpher Secures Strategic Investment from the Amazon Alexa Fund for Consumer Data Protection in AI

“We are excited to support Inpher as they continue to pioneer Secret Computing(R), further improving consumer and client confidence in data privacy and security controls.” said Paul Bernard, Director of the Amazon Alexa Fund

PR Newswire: Cornami Partners with Inpher, Pioneer in Secret Computing, to Deliver Quantum-Secure Privacy-Preserving Computing on Encrypted Data

“Massive security breaches bring regulatory pressure to restrict data collection and cloud-based information with the realization that regardless of effort, with existing perimeter-based security measures, computing environment attack points will always exist. The cryptography experts at Inpher have developed quantum-safe cryptographic primitives and protocols to encrypt data and perform arbitrary computation on that encrypted data that returns an encrypted result.” said Paul Master, CTO, and co-founder of Cornami

STARTUPTICKER: Cybersecurity Solutions from Switzerland on High Demand

“The increasing partnerships between Swiss cybersecurity startups and different organizations are a demonstration of the growing demand for securer solutions to accelerate digital transformation. The partnerships are starting to bear fruit with new solutions being launched on the market. An example is Inpher, which delivers quantum-secure privacy-preserving computing on encrypted data.”

JURIST: Post-Schrems II, Privacy-Enhancing Technologies for Cross-Border Data Transfers

“The data exporter wishes personal data to be processed jointly by two or more independent processors located in different jurisdictions without disclosing the content of the data to them. Prior to transmission, it splits the data in such a way that no part an individual processor receives suffices to reconstruct the personal data in whole or in part. The data exporter receives the result of the processing from each of the processors independently, and merges the pieces received to arrive at the final result which may constitute personal or aggregated data.”

NANALYZE: Inpher Preserves Data Residency With Secret Computing

“Companies can no longer just copy all their global data into a single data warehouse and run queries against it. There’s an increasing need to preserve data residency while manipulating data, making it challenging for machine learning algorithms that can’t work across disparate data sets. One company working to modernize Privacy-Preserving Machine Learning (ML) and data analytics experience for organizations is Inpher.”

GARTNER: Emerging Technologies and Trends Impact Radar: Security for Healthcare Providers

“HE is a cryptographic method that enables third parties to process encrypted data and return an encrypted result to the data owner while providing no knowledge about the data or the results. HE enables algorithm providers to protect proprietary algorithms and data owners to keep data private. We are just now seeing the emergence of FHE being offered by some vendors for specific use cases. A shortlist of prominent HE open-source projects include Torus-FHE (TFHE), PALISADE, SEAL, HElib, and HEAAN”. Inpher is one of the leading contributors to TFHE.

GARTNER: Emerging Technologies and Trends Impact Radar: Security in Manufacturing

“As the IIoT matures, stored sensor data collected through countless sensor nodes must be protected as intellectual property and other sensitive data are vulnerable to attack. HE is useful to provide encryption to sensor data that can be shared across an ever-growing automated supply chain for manufacturers, yet allow the third party to perform computations on the encrypted data. Sample Providers include Inpher, IBM, Microsoft, Galois, and Enveil

JUST SECURITY: Don’t Blame Privacy for Big Tech’s Monopoly on Information

“Current mainstream encryption methods are encryption at-rest (protecting information stored in your phone), and encryption in-transit (protecting emails and texts while they are being sent). Privacy-preserving cryptography builds on the third pillar of encryption: encryption in-use. This is the ability to perform analytics on someone else’s data in an encrypted format so that you can extract the insight you need without seeing the private data inputs. In the data-sharing context, this capacity would enable Big Tech to allow competitors to compute on its data without exposing any personal user information in the process.”

MORNING CONSULT: Privacy Does Not Pause in Pandemics

“In the absence of sufficient legal safeguards against data misuse, technical safeguards should be employed to automate data minimization, limited retention, and purpose limitation. Privacy-enhancing technologies that enable distributed computing — such as homomorphic encryption and secure multi-party computation — can help strike the balance of data utility and privacy whilst curbing data overreach. Built-in privacy is a failsafe where policy and regulations fall short.”

WATERS TECHNOLOGY: Banks Begin Exploring Homomorphic Encryption Use Cases

“‘Privacy computing or encryption can enable [users] to run forensics and patten matching on encrypted datasets to identify if there is suspicious trade behavior without actually exposing any of this individual investor information,’ [Inpher CEO Dr. Jordan] Brandt says.”

ACCENTURE: Maximize collaboration through secure data sharing

“…fintechs such as Inpher (with an investment from JPMorgan) have developed SMC products and services specific to the financial services industry.”

  • Author: Accenture Applied Intelligence

  • Published: October 1, 2019

  • Report Description: “In an increasingly AI-driven world, enterprises realize the importance of third-party partnership ecosystems to create new growth opportunities However, issues of trust, security and fear of losing competitive advantage prevent organizations from sharing data and collaborating with each other A new family of Privacy-Preserving Computation techniques will allow data to be jointly analyzed between parties without exposing all aspects of it Learn how these techniques are being used across industries⁠—and what Accenture’s doing to bring secure data sharing and enable greater collaboration.” Text from report description (available here).

  • Full Report PDF — Inpher mentioned on page 11: https://www.accenture.com/_acnmedia/PDF-114/Accenture-PPC-Techniques.pdf#zoom=4

SOLUTIONS REVIEW: Gartner Names 3 Cool Vendors in Privacy Preservation in Analytics for 2019

“Inpher lets you compute without ever exposing or transferring the underlying sensitive data. The firm’s XOR Secret Computing Engine is built from a proprietary advance in secure multiparty computation. Inpher utilizes encryption in-use technologies and securely distributes computation across multiple parties where no individual can see the other parties’ data. XOR meets and exceeds requirements for cross-border data transfer because the data never actually moves.”

WORLD ECONOMIC FORUM: The Next Generation of Data-Sharing in Financial Services: Using Privacy Enhancing Techniques to Unlock New Value

“…fintechs such as Inpher (with an investment from JPMorgan) have developed SMC products and services specific to the financial services industry.”

  • Authors: World Economic Forum and Deloitte, with numerous contributors

  • Published: September 12, 2019

  • Report Description: “This report explores an emerging set of technologies known as ‘privacy enhancing techniques’ (PETs), and their ability to unlock new value in the financial services industry by facilitating new forms of data-sharing. Sharing data would allow institutions to – for example – better detect fraud, offer customers more personalized advice, and proactively identify the buildup of systemic risks. However, these benefits historically have been in conflict with institutions obligation to keep their customers’ data private and their own information confidential. However, PETs have the potential to alter these dynamics, allowing institutions, customers, and regulators to enable the analysis and the sharing of insights without requiring the sharing of the underlying data itself.” Text from report description (available here).

  • Full Report PDF — Inpher mentioned on pages 14 and 19: http://www3.weforum.org/docs/WEF_Next_Gen_Data_Sharing_Financial_Services.pdf

GARTNER: Cool Vendors in Privacy Preservation in Analytics

“Inpher’s solution will be useful particularly to security and risk management leaders with a focus on activities to analyze and monetize multiple datasets across business operations, and/or datasets joined with other external data sources where personal data is involved. CIOs and chief data officers (CDOs) who apply an infonomics-based approach to data and data monetization, while facing regulatory privacy compliance requirements for privacy and data protection, should be interested. Also, data and analytics leaders who wish to preserve privacy in their daily activities may find the Secret Computing function relevant. Finally, (chief) compliance, risk, legal and data protection officers will want to know about the existence of a solution like that of Inpher.”

WALL STREET JOURNAL: The Morning Download: ‘Zero Knowledge’ Tech Catches JPMorgan’s Attention

“‘JPMorgan could use the ‘secret computing’ technology to analyze a customer’s proprietary data on their behalf, using artificial intelligence without sacrificing privacy,’ [Head of Data Analytics for the Corporate and Investment Bank] Mr. [Samik] Chandarana said. ‘This gives us a technological solution to be able to act on a client’s private data … without them having to worry about the security constraints or giving up all their information to us,’ he said.”

WALL STREET JOURNAL: JPMorgan Invests in Startup Tech That Analyzes Encrypted Data

“‘JPMorgan is testing the technology’s capabilities on its own internal datasets and expects to deploy it for customers within the next year. “We’re not making investments for a 5-year period. This is stuff we’re working on live now,’ [Head of Data Analytics for the Corporate and Investment Bank] Mr. [Samik] Chandarana said.”

PR Newswire: J.P. Morgan leads USD $10 million financing in leading data security and machine learning provider, Inpher

“Advances in data science and cryptography mean that we no longer have to accept the traditional tradeoff between security, privacy and usability when handling data of any kind. Inpher is at the vanguard of this revolution, and the practical applications for this technology are almost endless, not just in financial services,” said Samik Chandarana, Head of Data Analytics for the Corporate and Investment Bank at J.P. Morgan Chase.

WALL STREET JOURNAL: ING Belgium Sees Opportunities for ‘Secret’ Sharing of Encrypted Data

“It also could allow for the sharing of encrypted customer data between a bank and a telephone company, or a bank and a retail company — organizations that would not typically share data and could develop new products together, [ING Head of IT Platform Services] Mr. [Johan] Smessaert said.”