Inpher Announces Strategic Partnership with In-Q-Tel

May 17, 2022

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.

Flying Fuzzy: A Privacy Preserved No-Fly List for Global Airlines Using Fuzzy String Matching

March 10, 2022

Since 2020, airlines have been dealing with a rash of irate, disruptive, and violent passengers, resulting in many of those travelers being banned from their future flights. Yet each airline has its own no-fly list in a format that is unique to its carrier. This blog post shows how with Inpher’s XOR Platform, FAA and airlines can securely collaborate on identifying the no-fly passengers while protecting the traveler’s privacy and civil liberties.

Neuroimaging Based Diagnosis of Alzheimer's Disease Using Privacy-Preserving Machine Learning (Part 2)

February 21, 2022

This second of the three-part blog series describes two privacy-preserving machine learning models (linear and logistic regression) for detecting Alzheimer's disease. The models help the researchers understand AD's underlying mechanism, and the clinicians detect AD early on. We detail the workflow needed for training with the data coming from multiple private sources (hospitals, radiology labs or research institutes) using a combination of SPM tools for local preprocessing and Inpher's XOR Platform for Privacy-Preserving Machine Learning.

Neuroimaging Based Diagnosis of Alzheimer's Disease Using Privacy-Preserving Machine Learning (Part 1)

December 21, 2021

In this first of a three-part blog series, we present a viable solution designed by clinical researchers at CHUV and Inpher aiming to build privacy-preserving neuroimaging-based ML models of AD using Inpher’s XOR Platform. We specifically show how data providers can securely collaborate to build linear and logistic regression models that clinical and non-clinical researchers can use. With the rising hopes for efficient disease-modifying drugs, our clinically relevant concept of accurate ML-based diagnosis will help clinicians to efficiently stratify patients for clinical trials and finally deliver better care.

Building Privacy-Preserving Decision Tree Models Using Multi-Party Computation

December 7, 2021

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.

Privacy Challenges in Extreme Gradient Boosting

June 22, 2021

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.

How to Build Machine Learning Models with Private Data Sources on AWS

April 28, 2021

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.

Inpher Wins Tech4Trust 2021 Startup Accelerator Program

April 8, 2021

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).

EU and U.S. Policymakers Emphasize Privacy-Enhancing Technologies as a Shared Priority in 2021

February 26, 2021

Many consumers use phones and IoT devices that rely on FL every day without knowing what it is. This means that the novelty of FL is shielding scrutiny on its inherent—and new—security vulnerabilities coming to light. While FL is a useful technique to aggregate information held by millions of compute nodes, it needs an extra layer of privacy-enhancing technology to ensure there is no leakage of personal information through “model parameters” that expose sensitive inferences at the individual level.

The Privacy Risk Right Under Our Nose in Federated Learning (Part 1)

February 23, 2021

Many consumers use phones and IoT devices that rely on FL every day without knowing what it is. This means that the novelty of FL is shielding scrutiny on its inherent—and new—security vulnerabilities coming to light. While FL is a useful technique to aggregate information held by millions of compute nodes, it needs an extra layer of privacy-enhancing technology to ensure there is no leakage of personal information through “model parameters” that expose sensitive inferences at the individual level.

Inpher wins the iDASH Secure Genome Analysis Competition

December 9, 2020

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.

Accelerating Moore4Medical's Innovation: Ensuring Patient Privacy in Healthcare AI

November 19, 2020

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.

Better ESG Benchmarking with Secret Computing®

October 20, 2020

As asset flows continue to pour into environmental, social and governance (ESG) factored investments, the problem of establishing reliable benchmarks continues to persist. Investors and corporate managers now have the ability to incorporate PETs and Secret Computing® to generate better benchmarking that aligns sustained value with financial performance.

Sunny Kang, Sr. Privacy Counsel and Head of Policy at Inpher, published in Just Security

September 25, 2020

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.

A Call to Action: The Regulator’s Role in Supporting Privacy-Enhancing Technologies for Data-Driven Financial Crime Investigations

July 28, 2020

Combating financial crimes and protecting privacy are equally critical goals in modern society.

Financial crimes, when left undetected, can legitimize illegal profiting from systems of injustice on a global scale. Disregard for privacy can undermine an individual’s freedom to control how their personal information is used and disseminated. Risky data practices can further expose people to irreversible harms such as social profiling, identity theft, and data breaches.

Cofounder & CTO Dr. Dimitar Jetchev Featured on the Frontpage of Forbes Bulgaria

July 28, 2020

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.

Inpher’s Privacy-Preserving Cross-Border Analytics Case Study Published in Global FFIS Report

July 7, 2020

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.

Director of Security Innovation Mariya Georgieva presents at ETSI Security Week

June 23, 2020

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.

Inpher CEO Jordan Brandt interviewed on C-Suite Radio with Jeffrey Hayzlett

June 14, 2020

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 Report

May 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!

Fighting the Fraud Surge in the COVID-19 Crisis

April 30, 2020

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 Response

April 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.

An Alternative to Big Tech Breakup: Secure, Private Data-Sharing

February 7, 2020

Is there a good alternative to breaking up Big Tech? Yes, and the solution is secure, private data-sharing. With Secret Computing®, Big Tech companies could securely share their internal data with smaller competitors without ever sending or revealing their sensitive data in the process. This would effectively reduce Big Tech market power while maintaining the highest privacy standards.

Cofounder & CTO Dr. Dimitar Jetchev presents at Real World Crypto in NYC

January 15, 2020

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.

Inpher wins International Encrypted Data Competition

October 28, 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 Named a 2019 Gartner Cool Vendor in Privacy Preservation in Analytics

September 10, 2019

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!

Inpher Wins People's Choice Award at FCA Financial Crime TechSprint

August 9, 2019

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.

Predictions with Privacy for Patient Data

July 16, 2019

It has been proven that de-identified data points on anything from credit card transactions to healthcare records can be reidentified, often quickly, by trained data scientists with access to additional data points. A study conducted in 2000, for example, found that 87 percent of the U.S. population can be identified using a combination of their gender, birthdate and zip code.

NYC FinTech Innovation Lab Selects Inpher for 2019 Program

June 10, 2019

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.”

Inpher Finalist for Privacy-Preserving Genomic Data Competition

October 22, 2018

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

Why Encryption of Corp Data is a Powerful but Underutilized Tool

June 7, 2018

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.

Partnering for Privacy-Preserving Predictive Maintenance

April 24, 2018

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.

Playing with Encrypted Data - Without Seeing It

March 26, 2018

"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."

Securing Autonomous Fleets with Global-Trained Localized Brains

March 18, 2018

It is possible to train machine learning models with private data sets so that no single data point is identified but statistical learning is maintained, including outliers like balls bouncing in the road or black ice conditions. Just like with humans, the more you experience, the more you know how to react in the future.

Deep Learning in Finance

March 8, 2018

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.

Financial Cryptography 2018

February 21, 2018

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.

AI in Fintech Forum at Stanford

January 26, 2018

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

The Spectre of Hardware Security Looming over Intel SGX

January 16, 2018

As the fallout of the Spectre and Meltdown vulnerabilities settles, the future of in-silica security becomes fuzzier. There are many comprehensive reports on the attack vectors, patches and respective performance degradation, perhaps most lucidly presented by Peter Bright at Ars Technica.

Fintech Startups Selected for Presentations at Trustech

November 30, 2017

Alexander Petric presents "Touch But Don't See; Applications of Encrypted Search and Computing" at Trustech in Cannes.

Inpher Chosen for Thales Cybersecurity Program at Station F

October 12, 2017

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.

Inpher Selected for Investment Banking 4.0

September 18, 2017

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

Inpher a Finalist for Banking Cybersecurity Innovation Award

June 16, 2017

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.

ING Sees Opportunities for Secret Sharing of Encrypted Data

June 1, 2017

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.

US Senator Encourages Use of 'Privacy Enhancing Technologies'

May 23, 2017

In a letter to the Commission on Evidence-Based Policymaking, US Senator Ron Wyden, D-Ore. proposed the use of privacy enhancing technologies (PETs) by government agencies in order to protect sensitive data.

Inpher to present at Temenos Community Forum in Lisbon

April 21, 2017

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.

ING and Inpher Collaborate through Fintech Village

March 17, 2017

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.

Banking on [Digital] Trust

February 8, 2017

Trust cements the foundation of the banking industry. Without it, we would be more apt to keep cash stuffed under our mattresses than in the impenetrable vault of a stranger. Modern digital banking wins and maintains customers' trust based on the security, transparency and accessibility of their data. Unfortunately that trilogy is not always mutually inclusive.

Inpher Selected as Top 5 Finalist for Startup of the Year

January 6, 2017

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.

Behavioral Futures and Surveillance Capitalism

December 30, 2016

The inevitable onslaught of targeted advertisements has both consumers and technology companies wondering whether there is any alternative future for internet economics. Jonathan Shaw recently published a compelling piece in Harvard Magazine, breaking down some of the biggest challenges to our understanding of individual freedoms and technological progress.

Chief Scientist Nicolas Gama wins best paper at AsiaCrypt

December 6, 2016

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.

Confusion in China's Cyber Laws

November 8, 2016

The latest in a wave of sovereign data security laws has emerged from China, causing some alarm with companies trying to understand how it could impact their businesses. Several sectors are identified as "critical information infrastructure", including telecommunications, information services and finance, who would be required to store personal information and sensitive business data in China.

_ultra Encrypted Query Module launched on Temenos Marketplace

October 3, 2016

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.

Swiss Fintech Corner at Sibos

September 28, 2016

Inpher's CEO was interviewed as a selected startup at Sibos 2017

Extracting Value for Corporate Banks from Blockchain

September 27, 2016

"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.

24 FinTech Start-ups Featured at Largest Financial Event

September 5, 2016

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.

Cloud Security by the Numbers

August 22, 2016

With over 3,000 IT professionals surveyed, the recent Ponemon study sponsored by Gemalto addressed issues concerning the "Global State of Cloud Data Security." The webcast can be viewed here and the report can be downloaded here. The participants represented a good cross section of company scale and geographic location around the world.

Open Camps Conference at UN

July 12, 2016

The world's largest mission-driven open source conference, Open Camps aims to "break down barriers to technology innovation through open source governance, communities and collaboration." The Inpher team presented the _ultra development platform for application-level security and privacy at the Search Camp session in New York on July 10th.

Analytique et chiffrement au menu du Digital Circle

June 29, 2016

Dimitar Jetchev, CTO d’, une jeune pousse basée entre San Francisco et l’EPFL.

Gone, Not Forgotten.

June 16, 2016

Strong privacy laws that establish the 'right to be forgotten' may be unenforceable. EU citizens can request that search engines remove results that are no longer relevant or accurate; however, researchers at NYU have found that even after links are delisted it is possible determine the names of individuals who petitioned for their removal.

[Secure] Sharing is Caring

June 5, 2016

Keyword search is enabled on shared data by utilizing a key exchange system based on standard public and secret key cryptography. The _ultra encrypted key architecture allows applications to manage information in vulnerable cloud or on-premise environments while keeping sensitive data unreadable to the infrastructure provider and host.

Rethinking IoT Security

March 17, 2016

With over 20 billion devices coming online by 2020 and an estimated 25 vulnerabilities per product, it's no wonder that IoT security is a hot topic. While acknowledging that encryption is not the complete answer, we maintain that data should be protected as it is created.

Safe Harbor 2.0 and the reaction of cyber imperialists

March 7, 2016

Mr. Schrems has his doubts about 'Safe Harbor 2.0', according to his recent interview with Ars Technica. Others have been quick to jump on board with dissent, eyeing opportunities to become a neutral data haven. According to John Whelan, a data privacy lawyer, in an interview with the Irish site, “If Privacy Shield doesn't work out and ultimately data has to be segregated."

Attack of the Ombudsperson

March 2, 2016

The draft document for ‘Safe Harbor 2.0’ was released on March 2, and is pending review and approval by the EU Article 29 Working Party by the end of March (sure). Sidley Austin’s Data Matters blog covers it well. In summary, the new framework is ‘significantly different’ from Safe Harbor 1.0 so companies must re-certify to “ensure a level of protection of personal data..."

Inpher Launches with $1m Financing Round

February 26, 2016

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.

EU's GDPR Could Cost Firms up to $20m (4% of Global Revenue)

February 5, 2016

Firms are spending tens to hundreds of millions building new data centers in the EU to comply with post safe-harbor regulations in order to avoid hefty fines; up to $20m USD or 4% of global revenues. Despite their best efforts, employees' unsanctioned use of cloud applications that contain personal data could still render companies liable.