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Assistant Professor (f/m/d) – in the area of social data science and … – Times Higher Education

July 16, 2023 | Author: admin

Starting date: January 1, 2024

Application deadline: Review of applications will begin September 15, 2023, and will continue until the position is filled

Full Or Part Time: Full-time (40 hours/week)

Location: Vienna, Austria

The Department of Network and Data Science invites applications for a Full-Time Faculty Position at the assistant professor level in the area of social data science and social network science.

The Department of Network and Data Science is an interdisciplinary unit, integrating natural science and social science approaches. Further information about the Department of Network and Data Science is available at: http://networkdatascience.ceu.edu

Qualifications:

We expect applications from social data scientists, social network scientists, or quantitative social scientists. The candidates should have strong motivation to conduct interdisciplinary research and be interested in participating in projects with several departments at CEU. Capability of high-quality teaching is assumed.

Candidates should have a PhD in social sciences (sociology, political science, communication science, computational social science, social network science, etc.) as well as an excellent record of publications in international peer-reviewed journals, proof of successful teaching, and good recommendations.

Teaching load is comparable to that of research universities. The normal teaching load at CEU is 12 taught US credits per academic year, plus thesis supervision.

Compensation:

We offer a competitive yearly salary of 62.500 EUR, as well as additional benefits (e.g., pension plan). The initial contract is for six years. The contract can be turned into an indefinite contract depending upon review.

How to apply:

Applicants need to submit:

Review of applications will begin September 15, 2023 and will continue until the positions are filled.

Questions of academic nature may be addressed to Janos Kertesz, Head of the Department of Network and Data Science (kerteszj@ceu.edu)

Please send your complete application package to:

advert045@ceu.edu - including job code in subject line: 2023/045

CEU is an equal opportunity employer and values geographical and gender diversity, thus encouraging applications from women and/or other underrepresented groups. Since CEU strives to increase the share of women in professorial positions, given equal qualifications, preference will be given to female applicants.

CEU recognises that personal and family circumstances shape the trajectory of ones career and working patterns. We encourage applicants to detail periods of leave, part-time work or other such situations in their applications so that the Search Committee can assess an applicants academic record fairly in the context of their circumstances. Any declaration of personal and family circumstances is voluntary and will be handled confidentially and only considered in so far as it impacts on the academic career of an applicant.

To contribute to CEUs monitoring efforts to improve gender equality in the academic body, we kindly ask you to fill in this form with your gender identity. The provision of this information is optional and will be used for statistical purposes only.

The privacy of your personal information is important to us. We collect, use, and store your personal information in accordance with the requirements of the applicable data privacy rules, including specifically the General Data Protection Regulation. To learn more about how we manage your personal data during the recruitment process, please see our Privacy Notice at: https://www.ceu.edu/recruitment-privacy-notice-austria

About CEU

One of the worlds most international universities, a unique founding mission positionsCentral European Universityas both an acclaimed center for thestudy of economic, historical, social and political challenges, and a source of support forbuilding open and democratic societies that respect human rights and human dignity.CEU is accredited in the United States and Austria, and offers English-language bachelor's,master's and doctoralprograms in the social sciences, the humanities, law, environmental sciences,managementand public policy.CEU enrols more than 1,400 students from over 100 countries, with faculty from over 50 countries.

In 2019 CEU relocatedfrom Hungary to Austria asthe Hungarian governmentrevokedits ability to issue US-accredited degrees in the country. As a result, CEUoffers all of its degree programs inVienna, Austria;andretainsa non-degree, research andcivic engagementpresence in Budapest, Hungary,through itsCEU Democracy Institute, theInstitute for Advanced Study, theCEU Summer SchoolandThe Vera and Donald Blinken Open Society Archives (OSA),anditsHungarian language public educational programs and public lectures.

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Kinetica Now Free Forever in Cloud Hosted Version; Accelerate the … – insideBIGDATA

July 16, 2023 | Author: admin

Kinetica, the database for time & space, announceda totally free version of Kinetica Cloudwhere anyone can sign-up instantly without a credit card to experience Kineticas generative AIcapabilities to analyze real-time data. No other analytic database offers this pricing model with free storage and compute, and no expiration date. Unlike other offerings that expire after a trial period, provide limited functionality or require that the customer pay for infrastructure and storage, Kinetica Cloud is totally free forever for up to 10GB of data with full Kinetica database functionality, including Kineticas SQL-GPT, using Generative AI to turn language into SQL that executes quickly.Additionally, developers will be able to upgrade to Kinetica Cloud for Dedicated Workloads with support for production deployments, including dedicated compute resources and Kinetica Standard Support.

Deloitte estimates that devices capable of sharing their location will represent40% of all data by 2025,making spatiotemporal data where objects are and where they are moving the fastest growing segment of big data. Prime examples are streams of data from mobile devices, static or moving sensors, satellites, and video feeds from drones and closed-circuit TVs. Applications based on real-time spatial and time-series data are driving digital transformation across industries, including fleet management, supply chain transparency, connected car, precision agriculture, smart energy management, retail proximity marketing, and many others.

As big data evolves from web logs that capture human interactions with social media apps and the web to the next generation of extreme data that captures machine observations from sensors and cameras, the old technologies and methods once again must be reconsidered. For instance, joins between two spatial data sets and calculating overlapping polygons will cripple a traditional data warehouse or data lake. Add a time series function on top of that, and chances are the query will never complete. Further, most high-value use cases come from making decisions in real-time. Data warehouses and data lakes simply werent designed to solve complex problems in a real-time latency profile.Kinetica applies an innovative compute paradigm, commonly referred to as vectorization, to radically reduce the complexity and increase the scale and performance of spatiotemporal workloads.

Kinetica Cloud Free Forever includes:

Store and analyze up to 10GB of data with full Kinetica database functionality

Kinetica SQL-GPT functionality, using Generative AI to turn language into SQL that executes quickly

Unlimited SQL queries and API access

Unlimited SQL workbooks

KineticaCommunity support

Kinetica Cloud Free Forever complements existing Kinetica deployment options, including Developer Edition, deploying Kinetica through the AWS and Azure Marketplaces as a managed service, or deploying Kinetica software-only in a customers own cloud environment.

Additionally, the Kinetica Cloud for Dedicated Workloads option removes the data limits, giving customers more flexibility to choose from different clusters for hot data sizes depending on the size of their data and complexity of the queries issued. This option also features dedicated clusters and compute resources, ensuring customers consistent performance based on the size of the cluster they choose. Kinetica Standard support is also included.

Kinetica Clouds Free Forever version is a significant release for companies to easily experience working with the Kinetica database without having to deal with trial licenses or PoC budget approvals,said JohnOBrien, Principal Advisor and CEO, Radiant Advisors. Proven industry leading performance and multi-modal analytics features aside, our database benchmark engineers frequently commented how impressed they were with the Kinetica Cloud environment and analytics workbench in user experience and its ability to dramatically reduce development time for spatial and time-series analytics.

Were seeing two massive opportunities collide for our customers. First, real-time decisions informed by smart sensors and devices are increasingly critical in many industries and applications. Second, new approaches to data management and analysis must be taken as more of the data from such devices is streaming data that includes information about both time and location, said Philip Darringer, Vice President of Product Management, Kinetica. By removing cloud cost concerns for developers, were accelerating the transition to the next phase of big data, allowing organizations to scale the collection, analysis, and operationalization of this new form of location-enriched data for innovation, coupled with SQL-GPT functionality for easy and fast conversational querying on that data.

Pricing and Availability

Kinetica Cloud is totally free forever for up to 10GB of data with full Kinetica database functionality and is immediately available. With Kinetica Cloud for Dedicated Workloads, available in Q3, users can get started for only $4.50/hour with an Extra Small cluster, suited for hot data sizes of 250GB, or scale up to larger sizes as needed.

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Baffle Delivers End-to-End Data Protection for Analytics – GlobeNewswire

July 16, 2023 | Author: admin

SANTA CLARA, Calif., July 11, 2023 (GLOBE NEWSWIRE) -- Baffle, Inc. today unveiled Data Protection for Analytics, a single data security solution that provides end-to-end controls for data ingestion and consumption, making it easier for analytics and data science professionals to meet compliance requirements without high deployment or management overhead.

Data is invaluable to modern business. It informs decisions and is directly tied to revenue and profitability. As more teams and business units access data for analysis and decision-making, data sprawl has become a challenge with increased cost and risk. Also, the growing number of rigorous data privacy regulations add complexity, leading to potential fines or worse for companies that dont have proper controls over regulated data. However, implementation and ongoing management are often cumbersome, which causes delays or sometimes stops analytics projects altogether.

Baffle Data Protection for Analytics is the easiest and fastest way to secure analytics while meeting increasingly stringent compliance mandates. With no code changes, the platform encrypts, tokenizes or masks data as it is ingested into the most popular analytics databases and data warehouses to ensure a strong security posture when data is stored and moved through analytics pipelines. It also enforces access control policies and/or dynamic masking when data is accessed, ensuring true end-to-end protection of sensitive data.

Many teams are trying to secure their analytics data through a patchwork of third-party monitoring solutions, security exceptions that can delay projects, and procedural controls that do not account for errors or malicious attacks. This creates burdensome management overhead and leaves companies open to massive risk, said Ameesh Divatia, CEO and co-founder of Baffle. The Baffle platform removes implementation friction, making it easy to secure and manage regulated data in analytics.

End-to-End Data Protection for Analytics Baffle Data Protection for Analytics provides end-to-end controls for data ingestion, from applications into data stores, to consumption, from data warehouses for processing and analysis. Fine-grained access control ensures no unauthorized users, including cloud admins, database administrators, data analysts or data scientists, can access sensitive data in clear text. The data is kept in a fail-safe security posture, meaning unauthorized access leads to encrypted or masked data, which minimizes the risk of data breaches. Data is protected even when it is shared with another database, data warehouse or data lake. Baffle provides additional capabilities for completing analytic operations on encrypted data.

Baffle is designed for performance and scalability, minimizing impact on application and database performance. The solution offers more control and less risk with comprehensive key management along with bring your own key (BYOK) and keep your own key (KYOK) capabilities, which give companies full control over data, even in cloud data stores.

Visit the website to learn more about Baffle Data Protection for Analytics.

About Baffle

Baffle is the easiest way to protect sensitive data. We are the only security platform that cryptographically protects the data itself as its created, used, and shared across cloud-native data stores that feed analytics, applications and AI. Baffles no code solution masks, tokenizes, and encrypts data with no application changes or impact to the user experience. With Baffle, enterprises easily meet compliance controls and security mandates, and reduce the effort and cost of protecting sensitive information to eliminate the impact of data breaches. Investors include Celesta Venture Capital, National Grid Partners, Lytical Ventures, Nepenthe Capital, True Ventures, Greenspring Associates, Clearvision Ventures, Engineering Capital, Triphammer Venture, ServiceNow Ventures, Thomvest Ventures, and Industry Ventures.

Follow Baffle on Twitter and LinkedIn.

Contact: Jennifer TannerLook Left Marketingbaffle@lookleftmarketing.com

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Alteryx Analytics Automation powered by AWS allows CFOs to … – Help Net Security

July 16, 2023 | Author: admin

Alteryx announced decision intelligence and intelligent automation capabilities on AWS designed to empower chief financial officers (CFOs) and finance leaders to embrace cloud and data analytics as strategic tools for their modernization goals.

Analytic insights help us tailor digital transformation solutions based on our clients needs to achieve the greatest impact for their business, said Ana Margarita Albir, president at ADL Labs. Leveraging Alteryx and AWS, we are able to integrate capabilities across any data source, visualize and analyze data in real time, and enhance security, resulting in an estimated $6 million in cost and efficiency savings for both ADL and our clients.

Changing regulations and manual processes in the office of finance often mean repetitive work, time consuming data input, and hours of labor spent in preparing spreadsheets. The Alteryx intelligent automation capabilities available on AWS maximize the benefits of cloud for office of finance teams by modernizing processes that help them solve more complex data problems and adapt to constantly changing market environments. Highlights:

Impact the bottom line: Analytics automation significantly reduces the time and effort spent with manual processes, freeing up time for analysts on strategic projects moving the business forward. For instance, analysts on billing teams have used Alteryx to reduce the cost of overhead of manual bill reporting up to 25 percent. An accounting team reconciling financial data reduced time spent on reconciliation processes up to 99 percent.

Modernize with new technologies: Alteryx leverages the power of AWS to provide an environment where technologies like artificial intelligence, machine learning, data science, robotic process automation, and blockchain meet, making it easier for CFOs to quickly deploy and use new technology right away. For example, a finance team relying on labor-intensive, outdated tools for calculating sales tax liability can quickly adopt automation and configure alerts on incorrectly taxed transactions, freeing up tax resources for higher-value activities.

Gain data-driven insights at scale: Finance departments are expected to deliver regular insights to management for decision-making. Alteryx provides an automated process for connecting and combining different data sources, helping finance teams quickly process and transform large amounts of data so they can generate reports in a fraction of the time.

Digitally upskill across finance: Alteryx provides a self-service, low-code/no-code environment so that an analyst or business user can quickly upskill in data and analytics while leveraging the power and scalability of AWS.

Businesses globally are looking to automate for efficiencies and drive deeper insights to quickly respond to multifaceted challenges and dynamically changing landscape, said Nitin Brahmankar, VP, ISV and Global Ecosystem Partnerships, Alteryx. We are working with AWS to empower finance teams to leverage the power of the cloud and modernize financial processes to perform critical analysis that truly matters to their bottom line.

With Alteryx Analytics Automation powered by AWS, finance teams can innovate and modernize tax and audit processes with automated self-service analytics that streamline and accelerate traditional compliance work, said Madhu Raman, worldwide head of automation at AWS. Organizations can benefit from templates that help data analysts and line-of-business users to use, customize, extend, and integrate enterprise data with intelligent automation workflows that assist with record to report, procure to pay, and order to cash processes.

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Is blockchain the answer to data sciences global evolution – The Financial Express

July 16, 2023 | Author: admin

Demand for quality data seems to have increased with signs of a digital revolution incoming. Now, the question being asked is how technologies such as blockchain can contribute towards it. From what its understood, blockchain-backed data science can be key in validation of data sources. I believe blockchain can hold a position in data science as it offers a decentralised ledger for data storage and verification. In an era where data is considered invaluable, blockchains role in data science is believed to be paramount, Alankar Saxena, co-founder and CTO, Mudrex, a crypto-investing platform, told FE Blockchain.

Market studies suggest that blockchains influence on data science can tackle issues through application of statistics and machine learning. According to Medium, an online publishing platform, blockchains backing of data science can ensure benefits such as traceability, incorporated anonymity, large data scales, high data quality, among others. The platform also stated that blockchain can be a benefactor for data science associated with industries and business mechanisms.

As per an article by Forbes Technology Council, a professional networking community, blockchain can help with data around artists rights management, decentralised finance (DeFi), supply chain management, electronic health documents, among others. Reportedly, the platform has recognised 13 applications for blockchain-backed data science. I think data scientists can use non-fungible tokens to incorporate their findings on blockchain. This can enable us to get information based on fundamentals, on which the findings are based. Blockchain can ensure accountability for research organisations who issue reports, forecasts, among others, Rajagopal Menon, vice-president, WazirX, a cryptocurrency exchange, concluded.

In terms of cryptocurrencies, blockchain-backed data science can help identify investment patterns. Tata Consultancy Services, an information technology company, mentioned that data science can help cryptocurrency users get hold of new investment prospects, along with scrutiny of investment risks and anticipation of future developments. For example, Google Cloud, a cloud computing platform, supplies transaction histories for Bitcoin, Bitcoin Cash, Dash, Dogecoin, Ethereum, Ethereum Classic, Litecoin, and Zcash. Models around blockchain datasets include Elliptic Data Set, whichs a Bitcoin-based subgraph comprising 203,769 transactions and 234,355 directed payment flows. The dataset aims to create a sustainable cryptocurrency-based financial structure.

Data from Statista, a market research platform, highlighted that blockchain solution-based expenditure will reach $19 billion by 2024. Analytics Vidhya, a data science community, stated that by 2025, data analytics market will clock $21.5 billion, at a 24.5% compound annual growth rate (CAGR). Its been estimated that introduction of central bank digital currencies (CBDCs) will be crucial for data sciences future. As per Factspan Analytics, a business intelligence company, future usage of blockchain-backed data science will help deal with real-time fraud detection, data verification, encoded transactions, and distributed cloud storage. As industries and businesses adopt blockchain, we expect to see new use cases and applications emerge. Additionally, the integration of blockchain with other emerging technologies such as AI and IoT could lead to innovative solutions, Sumit Ghosh, co-founder and CEO, Chingari, a Web3.0 short video application, concluded.

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IIT Madras to Offer Data Science and AI Courses in IIT Zanzibar – Analytics Insight

July 16, 2023 | Author: admin

IIT Madras to offer data science and AI courses in IIT Zanzibar, headed by Preeti Aghayalam

Preeti Aghayalam will lead the first IIT to be built offshore, IIT Zanzibar, according to an announcement made by V Kamakoti, the director of the Indian Institute of Technology Madras (IIT Madras). According to the IIT-M director, academic classes at the international IIT Madras campus will start on October 24, 2023, and there will be a total of 70 seats available for BSc and MSc programs in data science and artificial intelligence (AI).

IIT Madras, ranked top overall for the previous five years by the Indian Ministry of Educations National Institutional Ranking Framework (NIRF 2023), has become the first Indian university to open a campus abroad. According to him, the Zanzibar Government will support the campus administration and has donated 200 acres of land to IIT Zanzibar. He also mentioned IIT Madras as the knowledge partner.

The director stated that the land needed to build the main campus will be ready by 2026 at the earliest. The IIT Zanzibar admissions process has already started. The institute will provide a 4-year BSc in Data Science and AI and a 2-year MSc in Data Science and AI as two full-time academic degrees.

It also has plans to introduce three new undergraduate programs during the following two years. Agribusiness-focused academic offerings and MTech in Cyber-Physical Systems are also anticipated to emerge soon. According to the fee schedule, candidates who want to enroll in IIT Zanzibars UG programs must pay US$12,000 annually, and those who wish to enroll in PG programs must pay US$4,000, not including hostel costs. The selection of students will be based on an interview round and a screening test created by IIT Madras faculty experts.

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Data Extraction Tool May Lead to Discovery of New Polymers – Datanami

July 16, 2023 | Author: admin

July 14, 2023 The amount of published materials science research is growing at an exponential rate, too fast for scientists to keep up. To help these scholars, a first-of-its-kind materials science data extraction pipeline is now available to make their research easier and faster.

Credit: Georgia Tech

The pipeline extracts material property records from published papers and populates the data into a new application called Polymer Scholar. The platform works like a browser to search polymers and materials properties by keyword, rather than reading through countless articles.

The application makes materials research more efficient, which could lead to discovery of new polymers.

Essentially, we have created an index on materials science literature that is much more granular than ones in a typical index that a search engine would create, said Georgia Tech Ph.D. studentPranav Shetty, the lead designer of the pipeline.

Our hope is that materials science researchers can make use of this capability in their day to day lives and workflows, and therefore, allow their work to have much more usability toward studying polymers and developing new materials.

The groups paper says the number of materials science papers published annually grows at a rate of 6% compounded annually. This amount of content makes for long, difficult work for scientists and in need of a computing solution.

The groups answer is MaterialsBERT, a model they built and trained that powers the pipeline.

MaterialsBERT categorizes words in text by association with a material property record. After the model associates text with records, the data is fed to Polymer Scholar. Scientists can use Polymer Scholar to study data, searching either polymer name or a property, like boiling point or tensile strength.

The group used 2.4 million materials science abstracts to train MaterialsBERT. In tests, the model outperformed five other models on three of five entity-recognition datasets.

According to the study, the pipeline needed only 60 hours to obtain 300,000 material property records from over 130,000 abstracts.

As a comparison, materials scientists currently use a database called PoLyInfo. This system has over 492,000 material property records, manually curated by hand over the span of many years. Georgia Techs pipeline can accomplish in hours what took humans years to do in PoLyInfo.

Polymer Scholar and MaterialsBERT are powered by a large corpus of 2.4 million materials science articles, which took some time and effort to develop the infrastructure to support such a large collection, said Chao Zhang, an assistant professor in the School of Computational Science and Engineering (CSE). This body of papers made all the difference training MaterialsBERT because it improved the language models ability to identify and extract data.

Polymer research is vital because of their role in manufacturing, healthcare, electronics, and other industries. Polymers have desirable properties that make them useful toward future applications. When polymer research slows, it inhibits development of new technologies. These technologies are needed to overcome todays challenges like climate change, faltering infrastructure, and sustainable energy.

In their paper, the group analyzed data using polymer solar cells, fuel cells, and supercapacitors as keywords in Polymer Scholar. This showed that scholars can use the pipeline to infer trends and phenomena in materials science literature. It also used practical examples to demonstrate applicability.

The journal npj computational materials published the groups paper because of its findings.

The groups work embodies Georgia Techs commitment to interdisciplinary scholarship. Researchers from the School of CSE and the School of Materials Science and Engineering (MSE) collaborated on the pipeline.

School of CSE authors include Shetty, Zhang, and Ph.D. studentSonakshi Gupta. MSE authors include postdoctoral researchersArunkumar Chitteth Rajan,Christopher Kuenneth, undergraduate studentsLakshmi Prerana Panchumarti,Lauren Holm, and ProfessorRampi Ramprasad.

The pipeline is the latest work for the group who are committed to applying computational methods to lead innovations in materials science.

Our long-term vision is to use the extracted data to train models that can predict material properties, Ramprasad said. Creating a pipeline to extract this data that can seamlessly feed into predictive models will ultimately lead to an extraordinary pace of materials discovery.

Source: Georgia Tech

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Service Providers, Security Researchers Again Warn UK Against Mandating Compromised Encryption – Techdirt

July 16, 2023 | Author: admin

from the once-you-break-it,-it's-broken dept

Pretty much everyone who isnt a UK legislator backing the Online Safety Bill has come out against it. The proposal would give the UK government much more direct control of internet communications. Supposedly aimed at limiting the spread of child sexual abuse material (CSAM), the proposal would do the opposite of its moniker by making everyone less safe when interacting with others via internet services.

While proponents continue to offer up nonsensical defenses of a bill that would compromise encryption, if not actually outlaw it, people who actually know what theyre talking about have been pointing out the flawed logic of UK regulators, if not promising to exit the UK market entirely if the bill is passed.

As the bill heads for another round of votes, entities that actually want to ensure online safety continue to speak up against. The group of critics includes Apple, which knows from first hand experience the negative side effects created by demanding broken encryption and/or client-side scanning.

[I]n a statement Apple said: End-to-end encryption is a critical capability that protects the privacy of journalists, human rights activists, and diplomats.

It also helps everyday citizens defend themselves from surveillance, identity theft, fraud, and data breaches. The Online Safety Bill poses a serious threat to this protection, and could put UK citizens at greater risk.

Apple urges the government to amend the bill to protect strong end-to-end encryption for the benefit of all.

Also speaking up (again), but probably not being heard (again), are encrypted communication services WhatsApp and Signal both of which have promised to stop offering their services in the UK if the Online Safety bill becomes law. Here are the statements given to the Evening Standard by WhatsApp, Element, and Signal:

If the Online Safety Bill does not amend the vague language that currently opens the door for mass surveillance and the nullification of end-to-end encryption, then it will not only create a significant vulnerability that will be exploited by hackers, hostile nation states, and those wishing to do harm, but effectively salt the earth for any tech development in London and the UK at large, Meredith Whittaker, president of not-for-profit secure messaging app Signal told The Standard.

[]

No-one, including WhatsApp, should have the power to read your personal messages, Will Cathcart, head of WhatsApp at Meta told The Standard.

[]

Element chief executive and chief of technology Matthew Hodgson told The Standard, The Online Safety Bill is effectively giving the Government the remit to put a CCTV camera in everybodys bedrooms, and the way people use their WhatsApp today is pretty personal people use messaging apps more than they communicate with people in person.

The Evening Standard also takes time to note some hypocrisy contained in the bill. Whatever burdens are placed on encrypted services wont affect the legislators pushing this bill. Theyll still be free from snooping, even if none of their constituents are.

The Online Safety Bill concerns only online messages sent by UK citizens and residents, but not anything sent on messaging apps by law enforcement, the public sector, or emergency responders.

This is handy, given that The Standard understands that up to half of Government communications are still being sent over consumer apps like WhatsApp.

The UK government continues to insist despite all the evidence it has provided to the contrary that its not interested in breaking encryption, installing backdoors, or otherwise undermining users privacy and security. But its protestations are inept and absolutely not backed by any of the wording in the bill, which contains mandates that would absolutely do the things the bills defenders insist it wont.

Theres no better demonstration of this form of bullshit than Conservative MP Damian Collins attempting to talk his way out from under the bills wording while debating Signals Meredith Whittaker, who continually points out the assurances Collins offers arent actually in the bill.

The opposition to the bill has gone from cacophonous to deafening in recent days. As Natasha Lomas reports for TechCrunch, a group of 68 security researchers have offered up their group opposition to the Online Safety Bill in a letter [PDF] that briefly, but incisively, points out the flaws in the legislation.

Heres that letters take on client-side scanning just one of several problematic mandates:

A popular deus ex machina is the idea to scan content on everybodys devices before it is encrypted in transit. This would amount to placing a mandatory, always-on automatic wiretap in every device to scan for prohibited content. This idea of a police officer in your pocket has the immediate technological problem that it must both be able to accurately detect and reveal the targeted content and not detect and reveal content that is not targeted, even assuming a precise agreement on what ought to be targeted.

[]

We note that in the event of the Online Safety Bill passing and an Ofcom order being issued, several international communication providers indicated that they will refuse to comply with such an order to compromise the security and privacy of their customers and would leave the UK market. This would leave UK residents in a vulnerable situation, having to adopt compromised and weak solutions for online interactions.

Thats actually the smaller (and shorter) of the two open letters issued in the past few days by security researchers. The second letter [PDF] contains seven pages of signatories from all over the world, as well as a more in-depth critique of the extremely flawed proposal.

The letter notes the issues scanning for CSAM using hashes already poses: namely, that hashes can be altered to avoid detection and that false positives still happen frequently. Now, take these existing problems, scale them to the nth degree, and throw some AI into the mix. This is whats awaiting UK residents if the bill passes with the client-side scanning/encryption-breaking mandates in place:

At the scale at which private communications are exchanged online, even scanning the messages exchanged in the EU on just one app provider would mean generating millions of errors every day. That means that when scanning billions of images, videos, texts and audio messages per day, the number of false positives will be in the hundreds of millions. It further seems likely that many of these false positives will themselves be deeply private, likely intimate, and entirely legal imagery sent between consenting adults.

This cannot be improved through innovation: false positives (content that is wrongly flagged as being unlawful material) are a statistical certainty when it comes to AI. False positives are also an inevitability when it comes to the use of detection technologies even for known CSAM material.

Not only will the government be able to sift through all of this, if anything gets flagged, it will also get to sift through all of these personal messages even when the AI is wrong about what it thought it had observed. Narrowly targeted scanning only in situations where some evidence already exists that CSAM is being distributed could limit the collateral damage, but nothing in the bill or in supporters statements indicate the government is interested in any process that doesnt give it the opportunity to collect it all.

Then theres the mission creep, which is always present when a government expands its surveillance powers.

Even if such a CSS system could be conceived, there is an extremely high risk that it will be abused. We expect that there will be substantial pressure on policymakers to extend the scope, first to detect terrorist recruitment, then other criminal activity, then dissident speech. For instance, it would be sufficient for less democratic governments to extend the database of hash values that typically correspond to known CSAM content (as explained above) with hash values of content critical of the regime. As the hash values give no information on the content itself, it would be impossible for outsiders to detect this abuse. The CSS infrastructure could then be used to report all users with this content immediately to these governments.

Even if the UK government would never do this (and no one believes it wouldnt), a Western nation with liberal values (as in enshrined human rights, etc.) passing this sort of law would embolden far less liberal nations to expand their domestic surveillance programs under the pretense of making the internet safer and/or detecting CSAM.

Whether or not all of this opposition will make a difference remains to be seen. So far, the steady stream of criticism and promises to exit the market havent managed to alter the bills mandates in any significant manner. Maybe the EUs recent abandonment of encryption-breaking mandates in its internet-targeting legislation following months of criticism will force UK lawmakers to rethink their demands. Then again, this is the same government that decided it didnt want to be part of any club that would accept it and Brexited its way into the wrong side of history.

Filed Under: client side scanning, csam, damian collins, encryption, meredith whittaker, online safety bill, ukCompanies: signal

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The battle over end-to-end encryption: Legislation threatens user … – CTech

July 16, 2023 | Author: admin

On July 20, a Nebraska court is expected to sentence Jessica Burgess, a 42-year-old woman who provided her 17-year-old daughter with pills for a late-term abortion, resulting in a two-year prison term. This case garnered attention as it raised concerns about prosecutors targeting individuals seeking abortions and those supporting them. It also underscores the privacy issues surrounding Meta's compliance with a search warrant, leading to the handover of private chats between the mother and daughter on Facebook to law enforcement authorities.

Meta's compliance was mandatory due to its possession of the data. However, this raises the question of why the company had possession of such data in the first place. Messenger is among the few major instant messaging services that lacks end-to-end encryption (E2EE) technology. Twitter also falls into this category, with its employees having been exposed to users' messages on the platform both actively and passively over the years.

Today, the greatest challenge to user privacy comes from governments striving to mandate technology companies to provide a "backdoor" for access to personal conversations in messaging applications. The most advanced legislation in this regard, supported by major industry players, is the British "Online Safety Bill," currently awaiting a vote.

This complex and comprehensive law addresses various important issues, including Section 110, which requires websites and applications to proactively prevent harmful content in messaging services. The legislation grants regulators the option to employ scanning technology (if available) to examine user communications and identify prohibited content. Based on the findings, various actions can be taken. The concern is that the legislation in the UK will establish a legislative precedent that other governments worldwide may follow, significantly undermining end-to-end encryption.

An open letter signed by the heads of seven messaging applications, including WhatsApp and Signal, stated, "If implemented as written, [this bill] could empower Ofcom to try to force the proactive scanning of private messages on end-to-end encrypted communication services nullifying the purpose of end-to-end encryption as a result and compromising the privacy of all users. In short, the bill poses an unprecedented threat to the privacy, safety and security of every UK citizen and the people with whom they communicate."

End-to-end encryption (E2EE) technology safeguards user privacy and security by encrypting digital communications, ensuring that only the sender and recipient can decrypt the messages. This means that even service providers like WhatsApp, Signal, or iMessage cannot read or listen to conversations. This technology already exists and is the default setting in many applications, enabling billions of users worldwide to communicate privately and securely.

While the British legislation is among the most advanced and comprehensive in addressing this technology, it is not the only one. In April, the United States introduced the federal bill "STOP CSAM (child sexual abuse material)" to protect children online. This legislation creates an exception to Section 230, which grants partial immunity to internet intermediaries for user-generated content related to child exploitation. The proposed bill holds technology companies civilly and criminally liable if their products are used to "promote or facilitate" crimes involving child exploitation and other offenses. It also requires companies to submit annual reports to the Federal Trade Commission (FTC), detailing the means and technologies they employ to protect users and any factors that may hinder their ability to identify instances of child exploitation.

According to the American digital rights organization the Electronic Frontier Foundation (EFF), the terms "promote" and "facilitate" are broad and allow for low-standard accountability. Such broad wording may lead to a surge of lawsuits by victims of child exploitation filing claims against tech companies. Consequently, courts may have to address questions like whether Signal facilitates child exploitation by defaulting to end-to-end encryption or whether Apple contributes to it by offering the application in its app store. Opponents of the law argue that these options provide sufficient room for negotiation and could incentivize tech companies to weaken encryption, compromising digital security for all internet users.

In the European Union, efforts are also underway to promote CSAM legislation. Proposed in May and currently in the legislative process, this regulation mandates instant messaging applications to install monitoring software that scans images and conversations based on a secret database. An independent entity overseeing the process determines the content and criteria for searching, and if prohibited content is detected, it must be reported to the authorities. This legislation not only compromises end-to-end encryption but also enables governments to directly access personal information.

Proponents of these various legislations claim that they are necessary to protect children. "We support strong encryption, but it cannot come at the expense of protecting the public," said a British government spokesperson in a statement. "End-to-end encryption cannot hinder efforts to apprehend the perpetrators of the most serious crimes." A leaked document from the European Commission in May, responding to the Commission's legal advice on the proportionality issue, stated that "the Commission believe that there are many elements which, especially when considered as a whole, justify the conclusion that the proposed search warrant system is proportional."

Civil organizations working in this field argue that existing encryption is not only vital for the fundamental right of individual privacy, but also serves as essential protection for the most vulnerable. They explain that it prevents personal information from falling into the wrong hands, especially for children, and enables human rights activists to operate in hostile environments. Moreover, providing a backdoor for accessing private communications creates a significant information asymmetry between governments and citizens, potentially increasing surveillance and control, leaving citizens more vulnerable to cyber attacks, and ultimately failing to address the problem of child exploitation.

Signal President Meredith Whittaker, in discussing the British bill, told the BBC, "It's magical thinking to believe that only the good guys can have privacy. Encryption protects everyone or it is broken for everyone."

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Machine-Learning Explore the Top 5 Entry-Level Machine Learning … – Analytics Insight

July 16, 2023 | Author: admin

Machine learninghas emerged as a rapidly growing field, revolutionizing various industries and transforming businesses operations. As a fresher with a passion for data and a desire to explore the world of artificial intelligence, several exciting entry-level job roles are waiting for you. This article will delve into the top five entry-levelmachine learning job rolesthat provide excellent opportunities for freshers to kick-start their careers. These job roles will pave u the way formachine learning jobs.

Amachine learning engineeris responsible for designing, building, and implementing machine learning models and algorithms. They work closely withdata scientistsand software engineers to develop robust and scalable solutions. As an entry-level machine learning engineer, your primary tasks may involve data preprocessing, model development, and performance optimization.

To excel in this role, proficiency in programming languages like Python, R, or Java is essential. Additionally, knowledge of machine learning libraries such as TensorFlow or PyTorch is highly advantageous. Building a solid foundation in statistics and mathematics will also prove beneficial in understanding the underlying principles of machine learning algorithms.

Data scientists are at the forefront of extracting insights and creating value from vast data. They are responsible for gathering, analyzing, and interpreting complex datasets to solve business problems. As a fresher, you can begin your journey as a data scientist by working on entry-level tasks such as data cleaning, visualization, and basic predictive modeling.

Proficiency in programming languages such as Python or R is crucial for a data scientist. Additionally, knowledge of statistical analysis, data visualization, and machine learning techniques is vital. Familiarity with tools like Jupyter Notebook, SQL, and Tableau will provide an added advantage in this role.

Working as an AI research assistant can be an excellent opportunity for freshers aspiring to delve deeper into the world of artificial intelligence. In this role, you will collaborate with researchers and scientists in exploring innovative approaches to solve complex problems. You will be involved in literature reviews, experimentation, and the development of prototypes.

A strong foundation in mathematics and computer science is crucial to thrive as an AI research assistant. Knowledge of machine learning algorithms, deep learning architectures, and research methodologies will also be valuable. Strong analytical and problem-solving skills and proficiency in programming languages such as Python are essential.

As a machine learning analyst, your primary responsibility will be to analyze and interpret large datasets to extract meaningful insights. You will work closely with cross-functional teams to identify trends, patterns, and anomalies that can drive business decision-making. This role often involves applying statistical techniques and machine learning algorithms to identify opportunities and optimize processes.

To excel as a machine learning analyst, you should possess strong analytical skills and the ability to work with complex datasets. Proficiency in programming languages such as Python or R and data visualization tools like Tableau or Power BI will be advantageous. Familiarity with statistical analysis techniques and predictive modeling will also prove beneficial.

As an entry-level AI consultant, you will be crucial in guiding organizations in adopting and implementing AI-driven solutions. You will work closely with clients to understand their business requirements, assess their data infrastructure, and identify opportunities for integrating AI technologies. This role requires strong communication skills, as you must effectively communicate complex concepts to non-technical stakeholders.

To succeed as an AI consultant, you should understand machine learning algorithms, data analysis, and AI frameworks. Proficiency in programming languages such as Python and knowledge of cloud platforms and big data technologies will be valuable. Additionally, business acumen and the ability to work collaboratively are essential attributes for this role.

Entering the field of machine learning as a fresher opens up a world of exciting career opportunities. The top five entry-level job roles discussed in this article offer an excellent starting point for freshers looking to kick-start their careers in AI. Whether you become a machine learning engineer, data scientist, AI research assistant, machine learning analyst, or AI consultant, acquiring the necessary skills and staying updated with the latest advancements in the field will pave the way for a successful journey in machine learning.

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