The issue of data protection has become a hot topic during the 2021 World Artificial Intelligence Conference (WAIC), with workshops, forums and speakers pledging to enhance data security and exhibiting related technologies, after the removal of the “Chinese Uber” Didi Chuxing app from domestic app stores raised concerns and discussions about how data should be treated in cross-border business. 

Several speakers told the Global Times on the sidelines of WAIC that the trend of using technologies and services for data privacy protection while still unlocking the value of big data is “on a rapidly rising curve,” as artificial intelligence companies face tightened regulation requirements on data protection. 

A number of forums are dedicated to the discussion of creating a balance between unlocking the value created by big data and keeping the date safe through technical means.

One such technology is called the Privacy-Preservation Computation (PPC), which is a set of cutting-edge technologies that allow companies to analyze data jointly without seeing the specific data. 

For example, the technology will allow companies to know what tea brands 20-30 girls like to drink based on a pool of data, but they wouldn’t be able to see the original data of each person involved.

“I think China is leading in Privacy-Preservation Computation. Especially this year, many new companies have emerged in the field of privacy calculation, including companies that used to engage in data, safety business or even blockchain,” a person at the Ant Group booth told the Global Times. 

Ant is also using PPC technologies by “scale application,” while promoting the technology to other companies commercially, the person said. 

Ant Group demonstrated its AntChain Morse MPC platform. The platform utilizes cutting-edge Secure Multi-Party Computation technology, cryptography and privacy protection technology to ensure that all data cooperative parties can share data while keeping their original data private, which has been applied in intelligent risk management and joint scientific research.

Wang Shuang, founder of NVXClouds Tech, also a speaker at the WAIC, said that the market of privacy calculation has surged this year. 

Data also shows a booming trend of Chinese companies’ usage and sales of data privacy services. According to a report published by the National Industrial Information Security Development Research Center, the number of China’s privacy calculation patents has risen rapidly in the past two years, with most patents applied by Ant Group, Alibaba and WeBank.

The report showed that patent applications started to rise abruptly in 2017, and there were nearly 5,000 privacy calculation patents by the end of April. 

Such a trend is very likely to accelerate after Didi’s sudden removal from domestic app stores struck alarm bells among domestic companies that Chinese regulators would toughen management over data privacy protection, experts said. 

Didi’s shares slumped for four consecutive days after the removal. On Thursday, the company’s shares slumped by 5.37 percent to $11.27 on the New York Stock Exchange.

“Didi’s rectification should serve as an example of how Chinese regulators would supervise big data platforms’ data treatment model. In the future, the direction of such supervision would become clearer, which would exert a positive influence on national security as well as privacy protection,” Kuang Yuqing, founder of Lens Company Research and a veteran financial commentator, told the Global Times. 

The problem of data privacy protection has become more sensitive for cross-border flow of data, like the Didi case, as China and the US’ relations deteriorate amid US sanctions against Chinese tech companies, according to some speakers.

“There is a strong demand for PPC in cross-border data flow, especially among the unmanned vehicle enterprises and cross-border financial institutes,” Bian Yang, vice president of Fudata Technology, told the Global Times.

Wang also agrees that PPC technology can help companies share analyzed data abroad under government authorization and data compliance, but keep the source of the data at home.

But experts still pointed out some challenges in the current data privacy sector. An Guangyong, an expert at the China Mergers and Acquisitions Association, told the Global Times that in terms of data privacy protection, China still lacks regulations as well as technologies.

“In Western countries, the updating speed of credit and related laws is most frequent (among all laws), and I think China should also formulate relevant laws to regulate data privacy, while updating it from time to time,” he told the Global Times. 

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n an effort to assist lenders expedite the processing of functions for the U.S. Small Enterprise Administration’s (SBA) Paycheck Safety Program, which goals to maintain staff employed throughout the coronavirus pandemic, Google developed an AI resolution known as PPP Lending AI that integrates with present doc ingestion instruments. It’s accessible to eligible lending establishments by June 30.

As Google explains in a whitepaper, AI can automate the dealing with of volumes of mortgage functions by figuring out patterns that will take a human employee longer to identify. Particularly, PPP Lending AI can classify and extract information in essential paperwork earlier than readying paperwork for submission to the SBA.

PPP Lending AI, which Google says takes solely days to implement, is an answer in three elements.

The primary is the Mortgage Processing Portal, a web-based app that serves as a consumer interface and self-servicing middle. Along with offering administration views for mortgage officers and mortgage processors, it permits finish customers and mortgage candidates to create, submit, and look at the standing of their PPP mortgage.VB Transform 2020 Online – July 15-17, 2020: Be part of main AI executives at VentureBeat’s AI occasion of the yr. Register today and save 30% off digital entry passes.

The second piece of PPP Lending AI is the Doc AI PPP Parser, which permits lenders to make use of AI to extract structured data from mortgage paperwork submitted by the mortgage candidates. It’s constructed atop Google Cloud‘s Doc AI, a service that leverages optical character recognition, type parsing, and pure language processing to seize and enrich unstructured information.

The third is Mortgage Analytics, which lets servicers or lenders onboard structured historic mortgage information, carry out de-identification anonymization on delicate data, retailer it securely with fine-grained information entry management, and carry out queries on the info.

“Leveraging synthetic intelligence, we’ve created an end-to-end resolution that accelerates the time-to-decision on loans and helps inform lenders’ liquidity evaluation — from the preliminary utility submission to the underwriting course of and SBA validation,” wrote Google Cloud world monetary providers and options lead Christin Brown in a blog post. “The answer can also be geared up with Google’s safety capabilities, enabling lenders to satisfy coverage necessities and shield essential belongings.”

Google says lenders can converse with a Google Cloud account supervisor for extra data.

PPP Lending AI seems to skirt round a newly imposed U.S. Treasury and SBA rule prohibiting the submission of PPA loans ready by robotic course of automation (RPA), or AI programs that carry out repetitive, monotonous duties at scale with larger velocity and accuracy than people. The companies blamed RPA for overburdening E-Tran, the SBA’s digital mortgage servicing portal, and lowering its capabilities.

On Monday, E-Tran crashed minutes after the opening of $310 billion in further PPP funding. The funds had been accredited final week following the primary $349 billion spherical, which ran out in early April. That’s regardless of the truth that the SBA restricted utility submissions to 350 per hour and allowed banks with a minimal of 5,000 loans to bulk-file their functions.

PPP loans can be found to small companies that had been in operation as of February 15 with 500 or fewer staff, together with not-for-profits, veterans’ organizations, tribal issues, self-employed people, sole proprietorships, and unbiased contractors. Companies with greater than 500 staff in sure industries may apply for loans, in accordance with the SBA and Treasury.

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Researchers have designed a novel approach to use deep learning to improve their understanding of how proteins interact in the body. According to the team, their findings could result in more accurate structure models of protein interactions in various diseases and better drug designs that target protein interactions.

The study, conducted at Purdue University, US, designed a system called the docking decoy selection with voxel-based deep neural network (DOVE). This applies deep learning principles to virtual models of protein interactions.

“To understand molecular mechanisms of functions of protein complexes, biologists have been using experimental methods such as X-rays and microscopes, but they are time- and resource-intensive efforts,” said Daisuke Kihara, a professor of biological sciences and computer science in Purdue’s College of Science, who leads the research team. “Bioinformatics researchers in our lab and other institutions have been developing computational methods for modelling protein complexes. One big challenge is that a computational method usually generates thousands of models and choosing the correct one or ranking the models can be difficult.”

The researchers used DOVE to scan the protein-protein interface of a model and then distinguish and capture structural features of correct and incorrect models.

“Our work represents a major advancement in the field of bioinformatics,” said Xiao Wang, a graduate student and member of the research team. “This may be the first time that researchers have successfully used deep learning and three dimensional (3D) features to quickly understand the effectiveness of certain protein models. Then, this information can be used in the creation of targeted drugs to block certain protein-protein interactions.”

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Signal Sciences, the fastest growing web application security company in the world, announced the general availability of the first application protection solution for Istio, the industry-leading open source service mesh for microservices architecture (MSA) applications.

Signal Sciences continues to offer the first and only next-gen web application firewall (WAF) and runtime application self-protection (RASP) integration with both Envoy and Istio, protecting today’s modern applications in any cloud.

In today’s software-driven world, some high performing companies are deploying new code thousands of times a day, according to DORA’s 2019 Accelerate State of DevOps report.

Microservices usage has skyrocketed as these teams look to facilitate shorter development cycles, continuous releases and greater agility. Meanwhile, security management and visibility between services in a cloud-native application has become an increasingly complex task.

By integrating Signal Sciences at the service mesh, organizations can easily gain visibility into both east-west and north-south traffic, and protect their MSA applications without requiring development teams to implement code changes.

Furthermore, Signal Sciences’ attack detection — based on proprietary SmartParse technology — makes highly accurate, instantaneous decisions and does not need the constant tuning or configuration that legacy WAFs require.

These integrations drastically simplify the deployment of Signal Sciences on containerized applications in Kubernetes environments and enable organizations to scale their protection easily.

“Development teams are migrating their monolithic application architectures over to highly distributed, microservices foundations en masse,” said Jonathan DiVincenzo, vice president of product management at Signal Sciences.

“While this industry-wide shift enables faster innovation and reduces time-to-market for new applications and features, the increased speed also means security capabilities must keep up rather than slow down engineering teams. Our integrations with Istio and Envoy empower these teams, enabling them to deliver quick value without sacrificing their security or visibility.”

The Signal Sciences next-gen WAF and RASP technology delivers the only application security solution that works across any architecture and offers the most flexible deployment options.

Fast and easy to implement, it protects close to 30,000 cloud-native, legacy, and serverless applications and over a trillion production requests per month. Organizations across all industries deploy Signal Sciences to protect their most important web applications, APIs, and microservices against the full spectrum of threats.

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As defenders increasingly use machine learning to remove spam, catch fraud, and block malware, concerns persist that attackers will find ways to use AI technology to their advantage.

Machine learning continues to be widely pursued by cybersecurity companies as a way to bolster defenses and speed response.

Machine learning, for example, has helped companies such as security firm Malwarebytes improve their ability to detect attacks on consumer systems. In the first five months of 2019, about 5% of the 94 million malicious programs detected by Malwarebytes’ endpoint protection software came from its machine-learning powered anomaly-detection system, according to the company.

Such systems, and artificial intelligence (AI) technologies, in general, will be a significant component of all companies’ cyberdefense, says Adam Kujawa, director of Malwarebytes’ research labs.

“The future of AI for defenses goes beyond just detecting malware, but also will be used for things like finding network intrusions or just noticing that something weird is going on in your network,” he says. “The reality is that good AI will not only identify that it’s weird, but [it] also will let you know how it fits into the bigger scheme.”

Yet, while Malwarebytes joins other cybersecurity firms as a proponent of machine learning and the promise of AI as a defensive measure, the company also warns that automated and intelligent systems can tip the balance in favor of the attacker. Initially, attackers will likely incorporate machine learning into backend systems to create more custom and widespread attacks, but they will eventually focus on ways to attack other AI systems as well.

Malwarebytes is not alone in that assessment, and it’s not the first to issue a warning, as it did in a report released on June 19. From adversarial attacks on machine-learning systems to deep fakes, a range of techniques that general fall under the AI moniker are worrying security experts.

In 2018, IBM created a proof-of-concept attack, DeepLocker, that conceals itself and its intentions until it reaches a specific target, raising the possibility of malware that infects millions of systems without taking any action until it triggers on a set of conditions.

“The shift to machine learning and AI is the next major progression in IT,” Marc Ph. Stoecklin, principal researcher and manager for cognitive cybersecurity intelligence at IBM, wrote in a post last year. “However, cybercriminals are also studying AI to use it to their advantage — and weaponize it.”

The first problem for both attackers and defenders is creating stable AI technology. Machine-learning algorithms require good data to train into reliable systems, and researchers and bad actors have found ways to pollute the data sets as a way to corrupt the resultant system.

In 2016, for example, Microsoft launched a chatbot, Tay, on Twitter that could learn from messages and tweets, saying, “the more you talk the smarter Tay gets.” Within 24 hours of going online, a coordinated effort by some users resulted in Tay responding to tweets with racist responses.

The incident “shows how you can train — or mistrain — AI to work in effective ways,” Kujawa says.

Polluting the dataset collected by cybersecurity firms could similarly create unexpected behavior and make them perform poorly.

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