The real possibility of advancing intelligence through deep learning and other AI-driven technology applied to video is that, in the long term, were not going to be looking at the video until after something has happened. The goal of gathering this high level of intelligence through video has the potential to be automated to the point that security operators will not be required to make the decisions necessary for response. Instead, the intelligence-driven next steps will be automatically communicated to various stakeholders from on-site guards to local police/fire departments. Instead, when security leaders access the video that corresponds to an incident, it will be because they want to see the incident for themselves. And isnt the automation, the ability to streamline response, and the instantaneous response the goal of an overall, data-rich surveillance strategy? For almost any enterprise, the answer is yes.
New Jersey, United States,- Market Research Intellect aggregates the latest research on Machine Learning Software Market to provide a concise overview of market valuation, industry size, SWOT analysis, revenue approximation, and regional outlook for this business vertical. The report accurately addresses the major opportunities and challenges faced by competitors in this industry and presents the existing competitive landscape and corporate strategies implemented by the Machine Learning Software market players.
The Machine Learning Software market report gathers together the key trends influencing the growth of the industry with respect to competitive scenarios and regions in which the business has been successful. In addition, the study analyzes the various limitations of the industry and uncovers opportunities to establish a growth process. In addition, the report also includes a comprehensive research on industry changes caused by the COVID-19 pandemic, helping investors and other stakeholders make informed decisions.
Key highlights from COVID-19 impact analysis:
Unveiling a brief about the Machine Learning Software market competitive scope:
The report includes pivotal details about the manufactured products, and in-depth company profile, remuneration, and other production patterns.
The research study encompasses information pertaining to the market share that every company holds, in tandem with the price pattern graph and the gross margins.
Machine Learning Software Market, By Type
Machine Learning Software Market, By Application
Other important inclusions in the Machine Learning Software market report:
A brief overview of the regional landscape:
Reasons To Buy:
About Us:
Market Research Intellect provides syndicated and customized research reports to clients from various industries and organizations with the aim of delivering functional expertise. We provide reports for all industries including Energy, Technology, Manufacturing and Construction, Chemicals and Materials, Food and Beverage, and more. These reports deliver an in-depth study of the market with industry analysis, the market value for regions and countries, and trends that are pertinent to the industry.
Contact Us:
Mr. Steven Fernandes
Market Research Intellect
New Jersey ( USA )
Tel: +1-650-781-4080
Our Trending Reports
Industrial Vision Systems Market Size, Growth Analysis, Opportunities, Business Outlook and Forecast to 2026
Water Quality Monitoring Market Size, Growth Analysis, Opportunities, Business Outlook and Forecast to 2026
Crystal Oscillators Market Size, Growth Analysis, Opportunities, Business Outlook and Forecast to 2026
Car Audio Market Size, Growth Analysis, Opportunities, Business Outlook and Forecast to 2026
IT Training Market Size, Growth Analysis, Opportunities, Business Outlook and Forecast to 2026
The emergence of technologies such as AI and machine learning, along with sophisticated analytics, offers opportunities for smart manufacturers to transform their businesses radically to create new product and service offerings while maximising the efficiency of supply chains and processes.
Contemporary computing models such as Cloud and, increasingly, Edge computing release huge amounts of sensor- and device-related data, to help with decision-making.
To succeed, however, companies need to be able to leverage this vast trove of data. In far too many cases, much of the data that is produced at the Edge is discarded, rather than transferred to a core environment for long-term storage.
For now, at least, the sector is lagging many other industries when it comes to capturing this opportunity.
That is a key finding in a report written by Seagate based on IDCs surveys, called ReThink Data: Put More of your Business Data to Work from Edge to Cloud. That study, which surveyed 1500 global enterprises, delved into the performance of several sectors including manufacturing and revealed that the vast majority of data available to organisations simply goes unused.
Manufacturing, despite having one of the highest enterprise data centre footprints and high levels of instrument connectivity, is actually recording one of the lowest levels of data growth compared to other sectors. In part, this is because it is difficult to expand the capacity of all that on-premises infrastructure especially when compared to scalable Cloud infrastructure more common in other sectors.
The irony is that the methods, strategies, and processes that typically make manufacturers successful seem to stop on the shop floor. For an industry steeped in the heritage of automation and integration, evidence suggests that these lessons are lost when it comes to managing data.
The authors of ReThink Data found that somewhat counterintuitively, manufacturing had the lowest level of task automation in data management and the lowest rate for full integration (single platform) of data management functions.
Among the key findings of the report:
The disconnect between assets and data management is not only inhibiting manufacturers from maximising the return from their current plant and equipment, it is also stifling innovation and opening up opportunities for more nimble and aggressive competitors to grab lucrative niches in the market.
So why does this disconnect exist?
A study in 2018 by industry analyst IDC, looking into the integration of Information technology and operational technology, said that many assets on the factory floor remain disconnected.
IDC says that this speaks to a strategic weakness around core enterprise architecture and infrastructure, with the researchers explaining that much current legacy infrastructure simply wont be able to cope with the inevitable growth of connected assets entering the plant.
The temptation for many companies will be to try to implement ad hoc processes to connect and manage assets, leaving them unable to rely on the underlying infrastructure for comprehensive management.
IDC also found that, even for companies that want to solve these issues, there is another problem: their aging workforces were trained for a different era of manufacturing. Too many lack the hard and soft IT skills that are prerequisites for the shop floor of tomorrow.
The Importance of DataOps
As plant and equipment become more digitised, generating vastly more data, the successful manufacturers will be those who can connect the data creators (which can include people and machines) with the data consumers (such as C-Suite and general executives).
Its a discipline in IT referred to as DataOps.
Seagate argues that DataOps should be part of every data-management strategy, which should also include data orchestration from endpoints to the core, data architecture, and data security.
The mission of DataOps is to provide managers with a holistic view of data and to enable users to access and derive the most value from data whether it is in motion or at rest.
This is another area, however, where many manufacturers are lagging. The ReThink Data report reveals that less than a third of manufacturers indicated that they have fully or even partially implemented a DataOps capacity.
The development and deployment of this skillset need to accelerate if manufacturers are to remain at the crest of the competitive curve. DataOps capabilities are essential to leveraging emerging technologies such as AI and machine learning, and to correlate data from core, Cloud, and Edge data sources.
The work of DataOps teams allows data science and data analytics professionals to use technologies like AI to transform data into the information needed by decision-makers.
And as the ReThink Data Report explains, Being able to correlate data from disparate sources is a capability not easily available through other means. Because it is difficult, those organisations able to master it can expect to have an edge over the competition.
More than Technology
The authors of ReThink Data also make clear that DataOps is an important part of implementing the necessary cultural change required to implement new ways of working by facilitating the sharing of data and breaking down organisational silos.
To succeed, however, leaders still need to drive a strategy that implements global standards, global data architecture, and global data management and delivers access to the same analytical tools by global teams.
Seagate says that rolling back reporting functions to IT can provide global tools, capabilities, and solutions that every group can leverage.
The various groups within the enterprise should get out of siloed management of their own data, and allow the IT-instituted tools to do that globally. In doing so, the teams will be freed to make decisions based on insights from reliable, global, accessible pools of data.
The article was written by Andrew Birmingham for the Which-50 Digital Intelligence Unit for Seagate. Members of the DIU provide their insights and analysis for the benefit of our readers. Membership fees apply.
In the slow process of developing machine learning models, data scientists and data engineers need to work together, yet they often work at cross purposes. As ludicrous as it sounds, Ive seen models take months to get to production because the data scientists were waiting for data engineers to build production systems to suit the model, while the data engineers were waiting for the data scientists to build a model that worked with the production systems.
A previous article by VentureBeat reported that 87% of machine learning projects dont make it into production, and a combination of data concerns and lack of collaboration were primary factors. On the collaboration side, the tension between data engineers and data scientists and how they work together can lead to unnecessary frustration and delays. While team alignment and empathy building can alleviate these tensions, adopting some developing MLOps technologies can help mitigate issues at the root cause.
Before we dive into solutions, lets lay out the problem in more detail. Scientists and engineers (data and otherwise) have always been like cats and dogs, oil and water. A simple web search of scientists vs engineers will lead you to a lengthy debate about which group is more prestigious. Engineers are tasked with construction, operation and maintenance, so they focus on the simplest, most efficient and reliable systems possible. On the other hand, scientists are tasked with doing whatever it takes to build the most accurate models, so they want access to all the data, and they want to manipulate it in unique, sophisticated ways.
Instead of fixating on the differences, I find its much more productive to acknowledge theyre both immensely valuable and to think about how we can use each of their talents to the fullest capacity. By focusing on the things that unify data scientists and data engineers a dedication to timely, quality information and well-designed systems the two sides can foster a more collaborative environment. And by understanding each others pain points, the two teams can build empathy and understanding to make working together easier. There are also emerging tools and systems that can help bridge the gap between these two camps and help them meet more readily in the middle.
MLOps is an emerging area that applies the ideas and principles of DevOps practices to the data science and machine learning ecosystem. It lifts the burden of building and maintenance off of data engineers, while providing flexibility and freedom for data scientists. This is a win-win solution. Lets take a look at some common problems, and the tools that are emerging to more effectively solve them.
Model orchestration. The first major hurdle when trying to put a model into production is deployment: where to build it, how to host it, and how to manage it. This is largely an engineering problem, so when you have a team of data scientists and data engineers, it typically falls to the data engineers.
Building this system takes weeks, if not months time that the data or ML engineers could have spent improving data flows or improving models. Model orchestration platforms standardize model deployment frameworks and help make this step significantly easier. While companies like Facebook can invest resources in platforms like FBLearner to handle model orchestration, this is less feasible for smaller or emerging companies. Thankfully, open source systems have started to emerge to handle the process, namely MLFlow and KubeFlow. Both of these systems use containerization to help manage the infrastructure side of model deployment.
Feature stores. The second major hurdle to taking a model from the lab to production lies with the data. Oftentimes, models are trained using historical data housed in a data warehouse but queried with data from a production database. Discrepancies between these systems cause models to perform poorly or not at all and often require significant data engineering work to re-implement things in the production database.
Ive personally spent weeks building out and prototyping impactful features that never made it to production because the data engineers didnt have the bandwidth to productionize them. Feature stores, or data stores built specifically to support the training and productionization of machine learning models, are working to alleviate this issue by ensuring that data and features built in the lab are immediately production-ready. Data scientists have the peace of mind that their models are getting built, and data engineers dont have to worry about keeping two different systems perfectly in line. Larger corporations like Uber and Airbnb have built their own feature stores (Michelangelo and ZipLine respectively), but vendors that sell pre-built solutions have emerged. Logical Clocks, for example, offers a feature store for its Hopsworks platform. And my team at Kaskada is building a feature store for event-based data.
DataOps. Theres no experience quite like getting paged late at night because your model is behaving strangely. After briefly checking the model service, you come to the inevitable conclusion: something has changed with the data.
Ive had variations on the following conversation more times than I like to admit:
Finding the issue is like finding a needle in a haystack. Fortunately, new frameworks and tools are coming into place that set up monitoring and testing for data and data sources and can save valuable time. Great Expectations is one of these emerging tools to improve how databases are built, documented, and monitored. Databand.ai is another company entering the data pipeline monitoring space; in fact the company published a great blog post herethat explores in greater detail why traditional pipeline monitoring solutions dont work for data engineering and data science.
By using tools to reduce the complexity of asks and by growing empathy and trust between data scientists and data engineers, data scientists can be empowered to deliver without overly burdening data engineers. Both teams can focus on what they do best and what they enjoy about their jobs, instead of fighting with each other. These tools can help turn a combative relationship into a collaborative one where everyone ends up happy.
Max Boyd is a Data Science Lead at Kaskada. He has built and deployed models as a Data Scientist and Machine Learning Engineer at several Seattle-area tech startups in HR, finance and real estate.
New Jersey, United States,- Market Research Intellect aggregates the latest research on Data Science and Machine-Learning Platforms Market to provide a concise overview of market valuation, industry size, SWOT analysis, revenue approximation, and regional outlook for this business vertical. The report accurately addresses the major opportunities and challenges faced by competitors in this industry and presents the existing competitive landscape and corporate strategies implemented by the Data Science and Machine-Learning Platforms market players.
The Data Science and Machine-Learning Platforms market report gathers together the key trends influencing the growth of the industry with respect to competitive scenarios and regions in which the business has been successful. In addition, the study analyzes the various limitations of the industry and uncovers opportunities to establish a growth process. In addition, the report also includes a comprehensive research on industry changes caused by the COVID-19 pandemic, helping investors and other stakeholders make informed decisions.
Key highlights from COVID-19 impact analysis:
Unveiling a brief about the Data Science and Machine-Learning Platforms market competitive scope:
The report includes pivotal details about the manufactured products, and in-depth company profile, remuneration, and other production patterns.
The research study encompasses information pertaining to the market share that every company holds, in tandem with the price pattern graph and the gross margins.
Data Science and Machine-Learning Platforms Market, By Type
Data Science and Machine-Learning Platforms Market, By Application
Other important inclusions in the Data Science and Machine-Learning Platforms market report:
A brief overview of the regional landscape:
Reasons To Buy:
About Us:
Market Research Intellect provides syndicated and customized research reports to clients from various industries and organizations with the aim of delivering functional expertise. We provide reports for all industries including Energy, Technology, Manufacturing and Construction, Chemicals and Materials, Food and Beverage, and more. These reports deliver an in-depth study of the market with industry analysis, the market value for regions and countries, and trends that are pertinent to the industry.
Contact Us:
Mr. Steven Fernandes
Market Research Intellect
New Jersey ( USA )
Tel: +1-650-781-4080
Our Trending Reports
Process Analytics Market Size, Growth Analysis, Opportunities, Business Outlook and Forecast to 2026
3D Printing Filament Market Size, Growth Analysis, Opportunities, Business Outlook and Forecast to 2026
Air Brake System Market Size, Growth Analysis, Opportunities, Business Outlook and Forecast to 2026
Anti-Graffiti Coatings Market Size, Growth Analysis, Opportunities, Business Outlook and Forecast to 2026
Astaxanthin Market Size, Growth Analysis, Opportunities, Business Outlook and Forecast to 2026
President Donald Trump this week announced his intentions to ban US companies from transacting with Chinese payment and messenger app WeChat, leading to a spike in downloads for privacy-first messenger app Signaland reigniting the debate on decentralized platforms.
The US Presidents Thursday executive order is so far vague but any transaction with the platform from US companies will be prohibited. The order, which also bans such transactions with social media app TikTok will come into effect on September 20.
Talk of banning TikTok started this year because the Trump administration was worried about the app collecting users data and the potential links its parent company, ByteDance, has with the Chinese government.
The WeChat ban had also been on the cards for a while. The two countries continue to aggressively clash heads during the coronavirus pandemic.
But while the US government was warning of banning WeChat, downloads of private messaging app Signal were soaring in China, according to a CNBC report. Unlike other messaging appsTelegram, Facebook Messenger or WhatsAppSignal isnt banned in China.
So worried Chinese people living, studying, or doing business in the US can turn to the highly secretive app in place of WeChat to communicate with family and friends without fear of government snooping or being shut downfor now.
Signal downloads were also on the up in Hong Kong after Mainland Chinas new security law hit the region. Despite WeChats popularity in China, it doesnt have the same level of encryption as apps like Signal and WhatsApp.
But even though Signal is secure, it could technically be banned by the Chineseor USgovernment tomorrow. Thats why decentralized chat apps or messaging systems are a must, some say.
Decentralized chat would completely solve this problem [of government bans and snooping], said Brad Kam, the co-founder of Dapp Unstoppable Domains, a decentralized domain name registrar that has built an Ethereum-based decentralized chat app, Unstoppable Chat.
The issue is that user data is sitting on Tencents [WeChats owner] servers and gets passed directly to the Chinese government. If WeChat were a decentralized chat app, then users would control their own messages stored on P2P storage networks. Not even WeChat would be able to read the messages," he said.
He added that Signal can still shut off users and any messaging app can be hacked or shut down.
Communication channels should not be pawns for geopolitical games, he said. They should be utilities for the people of the world.
Chris Troutner, a Bitcoin Cash developer, also told Decrypt that decentralized messaging systems are the way forward.
His highly encrypted messaging system based on Bitcoin Cash transactions, bch-encrypt, went live this year. His team is working on refining it: an end-to-end encrypted messaging system that will look and feel a lot like email.
He said that although Signal and Telegram are highly praised for their privacy features, they could still be banned by governments.
Decentralized, open-source, and end-to-end encrypted, he added. You really need all three [with a messaging app].
Global Mobile Data Protection (MDP) Solutions Market Trends 2020-2026
This report includes the overall and comprehensive study of the Mobile Data Protection (MDP) Solutions market with all its aspects influencing the growth of the market. This report is exhaustive quantitative analyses of the Mobile Data Protection (MDP) Solutions industry and provides data for making strategies to increase the market growth and effectiveness.
The Global Mobile Data Protection (MDP) Solutions market 2020 research provides a basic overview of the industry including definitions, classifications, applications and industry chain structure. The Global Mobile Data Protection (MDP) Solutions market analysis is provided for the international markets including development trends, competitive landscape analysis, and key regions development status.
The key manufacturers covered in this report are Microsoft BitLocker,Cisco Systems,Broadcom (Symantec Drive Encryption),Intel,Gemalto,Hewlett Packard Enterprise,McAfee,Trend Micro,Sophos SafeGuard,Digital Guardian,Dell Technologies,Check Point Capsule (Legacy),EgoSecure Data Protection,Data Resolve,Center Tools,Lookout,ZIMPERIUM,Arxan Technologies,Guardsquare,Kaspersky Lab,WinMagic
Development policies and plans are discussed as well as manufacturing processes and cost structures are also analyzed. This report also states import/export consumption, supply and demand Figures, cost, price, revenue and gross margins.
In addition to this, regional analysis is conducted to identify the leading region and calculate its share in the globalMobile Data Protection (MDP) Solutionsmarket. Various factors positively impacting the growth of the Mobile Data Protection (MDP) Solutions market in the leading region are also discussed in the report. The globalMobile Data Protection (MDP) Solutions marketis also segmented on the basis of types, end users, geography and other segments.
The final report will add the analysis of the Impact of Covid-19 in this report Mobile Data Protection (MDP) Solutions industry.
The study objectives of this report are:
To study and analyze the global Mobile Data Protection (MDP) Solutions consumption (value & volume) by key regions/countries, product type and application, history data from 2014 to 2018, and forecast to 2024.
To understand the structure of Mobile Data Protection (MDP) Solutions market by identifying its various subsegments.
Focuses on the key global Mobile Data Protection (MDP) Solutions manufacturers, to define, describe and analyze the sales volume, value, market share, market competition landscape, SWOT analysis and development plans in next few years.
To analyze the Mobile Data Protection (MDP) Solutions with respect to individual growth trends, future prospects, and their contribution to the total market.
To share detailed information about the key factors influencing the growth of the market (growth potential, opportunities, drivers, industry-specific challenges and risks).
To project the consumption of Mobile Data Protection (MDP) Solutions submarkets, with respect to key regions (along with their respective key countries).
To analyze competitive developments such as expansions, agreements, new product launches, and acquisitions in the market.
To strategically profile the key players and comprehensively analyze their growth strategies.
Key points covered in this report:
This report provides pin-point analysis for changing competitive dynamics
It provides a forward looking perspective on different factors driving or restraining market growth
It provides a six-year forecast assessed on the basis of how the market is predicted to grow
It helps in understanding the key product segments and their future
It provides pin point analysis of changing competition dynamics and keeps you ahead of competitors
It helps in making informed business decisions by having complete insights of market and by making in-depth analysis of market segments
TABLE OF CONTENT:
Chapter 1:Mobile Data Protection (MDP) Solutions Market Overview
Chapter 2: Global Economic Impact on Industry
Chapter 3:Mobile Data Protection (MDP) Solutions Market Competition by Manufacturers
Chapter 4: Global Production, Revenue (Value) by Region
Chapter 5: Global Supply (Production), Consumption, Export, Import by Regions
Chapter 6: Global Production, Revenue (Value), Price Trend by Type
Chapter 7: Global Market Analysis by Application
Chapter 8: Manufacturing Cost Analysis
Chapter 9: Industrial Chain, Sourcing Strategy and Downstream Buyers
Reports And Marketsis part of theAlgoro Research Consultants Pvt. Ltd.and offers premium progressive statistical surveying, market research reports, analysis & forecast data for industries and governments around the globe. Are you mastering your market? Do you know what the market potential is for your product, who the market players are and what the growth forecast is? We offer standard global, regional or country specific market research studies for almost every market you can imagine.
Contact Us:
Sanjay Jain
Manager Partner Relations & International Marketing
18/10/2018Author: Prominence Global | Business, content marketing, Instagram, LinkedIn, LinkedIn, Marketing, Social Media
If youve noticed your engagement decreasing all of a sudden and your followers are dropping like flies, Im sorry to report, maybe you have become a victim of the dreaded social media shadow ban.
The shadow ban is now taking place across the big social media platforms including LinkedIn, Twitter, Facebook and Instagram.
And if its happened to you, you will know. You will see it and feel it in the content you are putting out therebasically because it will be like crickets.
Nothing.
So have you been shadow banned? Or do you feel like you might be a victim? Let me explain what it is, who it happens to and why.
Its a massive phenomenon at the moment and many people I know and are talking to have been hit by the shadow ban on one platform or another.
Heres whatWikipediahas to say about it:Shadow banning (also called stealth banning, ghost banning or comment ghosting) is the act of blocking a user or their content from an online community such that the user does not realise that they have been banned.
Now we all know how important social media is to our businesses in this day and age. So a shadow ban could be detrimental to your profile and online presence, not to mention the connection, engagement and possible sales we might miss out on because of one.
The bad thing with shadow banning is that you might not even know it has happened to you and you could even believe youve been a good social media poster, abiding by all the rules and simply trying to grow your following.
Thats why its called shadow banning because it done in secret.
This could be completely true, however, you might accidentally be doing some things that warrant the social media platforms shadow ban.
Ok, so lets talk about what a shadow ban is exactly.
What is a shadow ban on social media?
Shadow banning is a mannerin which social media platform stop spam from happening, which is perfectly fine.
Its used to stop spammers, keep feeds clean and their customers happy.
The different platform will have different ideals and elements of whats included as part of a shadow ban but generally its a softer approach to giving you a warning that youre not playing by their rules, rather than just banning you completely.
Facebook has been open with their bans in that past if you have been spammy by messaging too many people or liking too many accounts too quickly.
Sadly, you probably wont know youve been shadow banned, especially if you dont know what it is and are only researching it for the first time now.
You will keep going about your business and posting and its not until you notice your reach has gone down, your likes, comments and shares are also down and youre not getting new followers that youll think what the heck is going on here?!.
Mostly you will find the shadow ban is temporary and not for very long, but some social media sites like Reddit will ban you for good.
Basically in a nutshell, you will become invisible to new prospects and followers, especially in the hashtag feeds. You will still be seen by your current followers, which is why you will still get some engagement.
Who does it happen to?
Anyone! It can happen to you if you are being naughty or nice on social media, meaning if you are playing by the rules if can happen to legit accounts and if you are abusing the platform if can happening to you as well.
Its important to note here that its not a deliberate attempt by the social media platform to stop your posts from being seen and found, its not personal. It is a project they are all working on to make sure they provide the best experience for their users and people looking at your content.
So please dont take it personally. It can be an easy mistake and thats why its important for me to share this information with you.
Are you wondering if its happened to you?
Check your engagement levels, if they are down and you havent been getting as many likes, comments, shares and new followers, theres a good chance its happened.
So theres a very easy test you can do to find out if you have been shadow banned. Ask a few people who dont follow you to see if they can find your account and if they can see your post in the hashtag feed.
What you might have done to get shadow banned. Here are some simple answers.
There are plenty of things you might have done without realising that it was a bad move and now you are shadow banned.
How to make sure it doesnt happen to you
If you are being sensible and doing all the right things, a shadow ban wont affect you and it doesnt have to mean the end of your profile and presence. If you have been shadow banned, now you know what not to do!
The number one rule is to always share good content responsibly.
Another area most people dont look at is the terms and conditions of the social media platforms. It would be a good idea to run your eye over these to see what you are allowed to do. Lets take a look at the things you can do on social media to avoid being shadow banned.
First and foremost, play by the rules.
If you do get shadow banned there are few things you can do.
If you are unsure of the shadow ban and dont want it to happen to you, please reach out. If you particularly want to know if its happened on LinkedIn, I have a live training where I will be going over this and the changes to LinkedIn in more detail on Nov 27th, 2018, from 7pm AEST. Heres your link to join us.<>
There are other ways we can help you too. Here are 4 ways we can help you accelerate your lead generation results:
Its the road map to positioning your profile in the top 5% of the 550 million LinkedIn users currently active Click Here
Its our Facebook community where smart entrepreneurs learn to get more leads and smart ways to scale using LinkedIn Click Here
Every 8 weeks we have a new intake into our 12 week Influencer program Click Here for more details
If youd like to work directly with us to create new marketing opportunities send us a quick message Click here tell us a little about your business and well organise a time for a deeper chat
China has given the internet traffic blocking capabilities a big update and is now using more modern interception technology. This will further strengthen what is known as the The Great Firewall of China as it continues to censor and block content, websites and apps from access by users within China. The update to the censoring tools is believed to be more potent in restricting HTTPS traffic that uses new technologies like TLS 1.3 and ESNI (Encrypted Server Name Indication). This comes as a part of a new joint report published this week by iYouPort, University of Maryland, and the Great Firewall Report. These three organizations have been tracking Chinese censorship on the internet.
We confirm that the Great Firewall (GFW) of China has recently begun blocking ESNIone of the foundational features of TLS 1.3 and HTTPS. We empirically demonstrate what triggers this censorship and how long residual censorship lasts, say the authors of the report. The Transport Layer Security (TLS) standard is the basis of secure HTTPS, or Hypertext Transfer Protocol Secure protocol, which allows users to see who they are communicating with, but no intermediary can snoop in on the information being transmitted. This communication also includes the Server Name Indication (SNI), which Chinese censors will use to detect and block content, websites and apps.
TLS 1.3 introduced Encrypted SNI (ESNI) that, put simply, encrypts the SNI so that intermediaries cannot view it. ESNI has the potential to complicate nation-states abilities to censor HTTPS content; rather than be able to block only connections to specific websites, ESNI would require censors to block all TLS connections to specific servers. We do confirm that this is now happening in China! reveals the report.
Researchers say that the blocking can be triggered bidirectionally, which means a connection from outside China can be blocked by the firewall, as would a connection from a user in China to a destination outside the firewall. There is however a way, researchers say, to circumvent the new-found powers of the firewall. This can be deployed by the client or the server. Geneva (Genetic Evasion) is a genetic algorithm developed by those of us at the University of Maryland that automatically discovers new censorship evasion strategies. Geneva manipulates packet streamsinjecting, altering, fragmenting, and dropping packetsin a manner that bypasses censorship without impacting the original underlying connection, say the researchers. However, they do warn that this tool is a research prototype and does not provide any encryption, protection, data privacy and is not optimized for speed.
TikTok, the famous short-form video-sharing app is in the limelight these days. Especially now that the US government is after it and President Donald Trump has not only suggested imposing a ban on it, he has also given an ultimatum to ByteDance, TikToks parent company, to sell off TikTok to either Microsoft or any other American-based investor before 15th September 2020. If ByteDance fails to do so, then the US operations of ByteDance will stop immediately.
A while ago, in December 2019, ByteDance was alleged with a class-action lawsuit by the US. It was accused of collecting users data and invade their privacy by making them privy to be identified and their data getting misused.
However, there is a fun fact - The user data that TikTok collects is not something new, because other famous American apps do the same, and not only this, tech giants like Facebook have had to face huge scandals because of this issue. So, how can TikTok alone be accused so blatantly for something that its American competitors also do?
Now, with the current issue, Trump has been claiming that TikTok is owned by a Chinese company, and the data that it collects from the users can become a cause of national security concern. And that is the reinforcing factor why it should be banned from the US at least.
Sadly, these are the same people who are usually least bothered about the data mining that the American apps are so famous for doing, and this also suggests that maybe Trump is using national security as a cover to apply app censorship policy on a foreign app! Maybe, it is some kind of political posturing rather than genuine care for TikToks users in the US.
App censorship is nothing new. Many countries block apps in the name of national security, or for techno-nationalism so that the users data remains inside and can be used by the companies of that country only, rather than going out and be misused in the other countries. Some countries block foreign apps due to economic reasons too.
So, online censorship is not a novel idea and has been going around since the early '90s. Different democratic and authoritarian governments have been filtering and blocking undesirable or harmful content over the internet.
Now, if Trump is trying to do the same, it means that the US app censorship policy is not so different from what China does already! It also means that while Trump should have enforced better transparency policies from all the tech apps about their content moderation standards and their accountability towards their users, he is busy in using various ploy tactics to ban a foreign app. The US government should have created a new standard for privacy for foreign companies that work within the US borders rather than playing with words and ultimatums!
Read next: Trumps remarks on getting a cut from the sale of TikTok has infuriated the Chinese media as they call it an open robbery
