- Common Misconceptions About Differential Privacy - Nov 24, 2021.
This article will clarify some common misconceptions about differential privacy and what it guarantees.
- Federated Learning: Google’s Take - Nov 8, 2021.
This blog will be focusing on the work Google has been doing in the Federated Learning space.
- Save Sarah Connor with Data Science - Oct 25, 2021.
Data science and data privacy are deeply interwoven, and must be carefully considered by practitioners. In comparing the Safe Harbour and Expert Determination data obfuscation approaches, Safe Harbour has been very popular among data engineers but has fundamental limitations, where Expert Determination offers important advantages.
- Don’t Touch a Dataset Without Asking These 10 Questions - Sep 20, 2021.
Selecting the right dataset is critical for the success of your AI project.
- The Difficulty of Graph Anonymisation - Feb 25, 2021.
Lessons from network science and the difficulty of graph anonymization. A data scientist's take on the difficultly of striking a balance between privacy and utility in anonymizing connected data.
- When Machine Learning Knows Too Much About You - Nov 14, 2020.
If machine learning models predict personal information about you, even if it is unintentional, then what sort of ethical dilemma exists in that model? Where does the line need to be drawn? There have already been many such cases, some of which have become overblown folk lore while others are potentially serious overreaches of governments.
- Seven Steps for Migrating Sensitive Data to the Cloud: A Guide for Data Teams - Oct 29, 2020.
Cloud migration requires a careful planning process to ensure all systems work as they should. Use this checklist, sponsored by Immuta and TDWI, to learn seven best practices for data teams migrating sensitive data to the cloud.
- Data Protection Techniques Needed to Guarantee Privacy - Oct 2, 2020.
This article takes a look at the concepts of data privacy and personal data. It presents several privacy protection techniques and explains how they contribute to preserving the privacy of individuals.
- Breaking Privacy in Federated Learning - Aug 26, 2020.
Despite the benefits of federated learning, there are still ways of breaching a user’s privacy, even without sharing private data. In this article, we’ll review some research papers that discuss how federated learning includes this vulnerability.
- Must-read NLP and Deep Learning articles for Data Scientists - Aug 21, 2020.
NLP and deep learning continue to advance, nearly on a daily basis. Check out these recent must-read guides, feature articles, and other resources to keep you on top of the latest advancements and ahead of the curve.
- Introduction to Federated Learning - Aug 20, 2020.
Federated learning means enabling on-device training, model personalization, and more. Read more about it in this article.
- How “Anonymous” is Anonymized Data? - Aug 18, 2020.
As the collection of personal data democratized over the previous century, the question of data anonymization started to rise. The regulations coming into effect around the world sealed the importance of the matter.
- Reducing Re-Identification Risk in Health Data - Aug 17, 2020.
Want to learn more about our recommendations for strengthening privacy while preserving utility? Read Immuta's new whitepaper, "Reducing Re-Identification Risk in Health Data: A Guide to Three Privacy Enhancing Technologies", to get the inside scoop on the best privacy enhancing technologies.
- 10 Use Cases for Privacy-Preserving Synthetic Data - Aug 11, 2020.
This article presents 10 use-cases for synthetic data, showing how enterprises today can use this artificially generated information to train machine learning models or share data externally without violating individuals' privacy.
- Automating Security & Privacy Controls for Data Science & BI – Webinar - Jul 28, 2020.
Moving sensitive data to the Cloud introduces the possibility of exposing data teams to new levels of risk, making it challenging to manage and prepare sensitive data for data science and analytics. Join our live webinar, Automating Security & Privacy Controls for Data Science & BI, Aug 12 @ 1PM ET to learn how Immuta for Databricks enables you to maximize the value of your sensitive data.
- 10 Steps for Tackling Data Privacy and Security Laws in 2020 - Jul 22, 2020.
Data privacy laws, such as the CCPA, GDPR, and HIPAA, are here to stay and significantly impact everyone in the digital era. These steps will guide organizations to prepare for compliance and ensure they support the fundamental privacy rights of their customers and users.
- Scale sensitive data science and analytics with confidence - Jul 16, 2020.
Listen to this on-demand webinar and hear how WorldQuant Predictive derives insights from building models on sensitive data while maximizing value and minimizing risk.
- Privacy-preserving AI – Why do we need it? - May 29, 2020.
Various data privacy threats can result from the usual process of building and constructing data and AI-based systems. Avoiding these challenges can be supported by utilizing state-of-the-art technologies in the domain of privacy-preserving AI.
- Machine Fairness: How to assess AI system’s fairness and mitigate any observed unfairness issues - May 26, 2020.
Microsoft is bringing the latest research in responsible AI to Azure (both Azure Machine Learning and their open source toolkits), to empower data scientists and developers to understand machine learning models, protect people and their data, and control the end-to-end machine learning process.
- Federated Learning: An Introduction - Apr 15, 2020.
Improving machine learning models and making them more secure by training on decentralized data.
- Analyzing GDPR Fines – who are largest violators? - Mar 6, 2020.
Fines from the GDPR have been rolling in since its inception in 2018. This article investigates who are the largest penalty recipients by country, the amounts, and private individuals.
- Top 5 AI trends for 2020 - Jan 21, 2020.
We are all witnessing a staggering growth of AI technology with so many new benefits for people while also changing the way we live and work. As AI continues to grow, which applications will have a significant impact in 2020?
- The 4 Hottest Trends in Data Science for 2020 - Dec 9, 2019.
The field of Data Science is growing with new capabilities and reach into every industry. With digital transformations occurring in organizations around the world, 2019 included trends of more companies leveraging more data to make better decisions. Check out these next trends in Data Science expected to take off in 2020.
- Data Anonymization – History and Key Ideas - Oct 17, 2019.
While effective anonymization technology remains elusive, understanding the history of this challenge can guide data science practitioners to address these important concerns through ethical and responsible use of sensitive information.
- Beyond Explainability: A Practical Guide to Managing Risks in Machine Learning Models - Sep 20, 2019.
This white paper provides the first-ever standard for managing risk in AI and ML, focusing on both practical processes and technical best practices “beyond explainability” alone. Download now.
- The Death of Centralized AI and the Rise of Open AI - Aug 29, 2019.
Centralized AI is giving way to more democratic AI systems, which are becoming more and more accessible to data scientists, both through code and through open ecosystems.
- eBook: How to Enhance Privacy in Data Science - Aug 22, 2019.
Check out this eBook, How to Enhance Privacy in Data Science, to equip yourself with the tools to enhance privacy in data science, including transforming data in a manner that protects the privacy, an overview of the challenges and opportunities of privacy-aware analytics, and more.
- PySyft and the Emergence of Private Deep Learning - Jun 27, 2019.
PySyft is an open-source framework that enables secured, private computations in deep learning, by combining federated learning and differential privacy in a single programming model integrated into different deep learning frameworks such as PyTorch, Keras or TensorFlow.
- 3 Big Problems with Big Data and How to Solve Them - Apr 18, 2019.
We discuss some of the negatives of using big data, including false equivalences and bias, vulnerability to security breaches, protecting against unauthorized access and the lack of international standards for data privacy regulations.
- Get the guidebook for tackling data privacy & compliance - Mar 12, 2019.
This guidebook walks through the myths & realities of pseudonymization and working with personal data, and suggests data team processes for compliance.
- The 7 Myths of Data Anonymisation - Mar 12, 2019.
Anonymisation has always been rather seen as a necessary evil instead of a helpful tool. That’s why plenty of myths have arisen around that technology over the years.
- Breaking neural networks with adversarial attacks - Mar 7, 2019.
We develop an intuition behind "adversarial attacks" on deep neural networks, and understand why these attacks are so successful.
- Io-Tahoe releases enhanced Smart Data Discovery solution, with PII and Sensitive Data Discovery capability, enabling compliance with the California Consumer Privacy Act (CCPA) - Feb 25, 2019.
Io-Tahoe technology can track changes to the sensitive data landscape over time to understand how the PII and the sensitive data footprint is changing, enabling firms to continually keep track of their data on an ongoing basis.
- A Non-Compromising Approach to Privacy-Preserving Personalized Services - Jan 8, 2019.
Could one even achieve both high privacy and high utility? Yes, and we explain how.
- Cartoon: Halloween Costume for Big Data. - Oct 31, 2018.
We revisit KDnuggets cartoon looking at the appropriate Halloween costume for Big Data and its companion, No Privacy.
- Key Takeaways from AI Conference SF, Day 2: AI and Security, Adversarial Examples, Innovation - Oct 30, 2018.
Highlights and key takeaways from selected keynote sessions on day 2 of AI Conference San Francisco 2018.
- A Right to Reasonable Inferences - Oct 1, 2018.
As shown in this paper, individuals are granted little control over how their personal data is used to draw inferences about them. Compared to other types, inferences are effectively ‘economy class’ personal data in the General Data Protection Regulation (GDPR).
- Key Takeaways from KDD 2018: a Deconfounder, Machine Learning at Pinterest, Knowledge Graph - Sep 11, 2018.
Highlights and key takeaways from KDD 2018, 24th ACM SIGKDD conference on Data Science and Data Mining: including what is a deconfounder, how Pinterest approaches Machine Learning, Knowledge Graph for Products, and Differential Privacy.
- Weak and Strong Bias in Machine Learning - Jul 6, 2018.
With the arrival of the GDPR there has been increased focus on non-discrimination in machine learning. This post explores different forms of model bias and suggests some practical steps to improve fairness in machine learning.
- Cartoon: GDPR first effect on Privacy - May 30, 2018.
New KDnuggets Cartoon examines the first unexpected effect of GDPR on Privacy.
- How Not to Regulate the Data Economy - May 24, 2018.
The GDPR will affect not just tech companies but any company that handles customer data — in other words, every company. And it will affect the use of data throughout the world, not just in Europe...
- Why Data and Infrastructure are key to determining Customer Intent,
May 31 Webinar - May 22, 2018.
Join Yieldmo, an advertising technology company and learn how Snowflake and Looker unleashed the potential of their mobile ad engagement data and drove more impactful marketing for their clients.
- How to build analytic products in an age of data privacy - May 17, 2018.
Privacy-preserving analytics is not only possible, but with GDPR about to come online, it will become necessary to incorporate privacy in your data products.
Studies have shown that only 1% or less of total users click on privacy policies, and those that do rarely actually read them. The GDPR requires clear succinct explanations and explicit consent, but that’s not the situation on the ground right now, and it’s hard to see that changing overnight on May 25th.
- Top 10 Technology Trends of 2018 - Apr 13, 2018.
In this article, we will focus on the modern trends that took off well on the market by the end of 2017 and discuss the major breakthroughs expected in 2018.
- What Does GDPR Mean for Machine Learning? - Apr 4, 2018.
This post investigates how the GDPR, which comes into force at the end of May, will effect machine learning.
- Will GDPR Make Machine Learning Illegal? - Mar 14, 2018.
Does GDPR require Machine Learning algorithms to explain their output? Probably not, but experts disagree and there is enough ambiguity to keep lawyers busy.
- Exclusive Interview: Doug Laney on Big Data and Infonomics - Jan 25, 2018.
We discuss 3Vs of Big Data; Infonomics and many aspects of monetizing information including promising analytics methods, successful companies, main challenges; Information marketplaces and why data ownership concept is misguided, and more.
- Data Science in 30 minutes, Artificial General Intelligence, and Answers to your Questions - Jan 22, 2018.
I recently was on a "Data Science in 30 minutes webcast", but there were interesting ideas and questions we did not have time to cover adequately. Here is a summary.
- Privacy Software Analysis Project - Jul 17, 2017.
Experienced Java developer with statistics and/or data privacy background to review and analyze one or more open source projects in the data privacy space and write reports on the functionality of those projects.
- How GDPR Affects Data Science - Jul 17, 2017.
Coming European GDPR directive affects data science practice mainly in 3 areas: limits on data processing and consumer profiling, a “right to an explanation” for automated decision-making, and accountability for bias and discrimination in automated decisions.
- Data Science Governance - Why does it matter? Why now? - Jul 10, 2017.
Everyone is talking about GDPR, Data Governance and Data Privacy, these days. Here we discuss what is it and why does it matter.
- Anonymization and the Future of Data Science - Apr 11, 2017.
This post walks the reader through a real-world example of a "linkage" attack to demonstrate the limits of data anonymization. New privacy regulation, most notably the GDPR, are making it increasingly difficult to maintain a balance between privacy and utility.
- Google Got a Lot of Data About You - Mar 9, 2017.
This article will dive into six types of data that most big tech companies, and especially Google, gather about consumers.
- Machine Learning Meets Humans – Insights from HUML 2016 - Jan 6, 2017.
Report from an important IEEE workshop on Human Use of Machine Learning, covering trust, responsibility, the value of explanation, safety of machine learning, discrimination in human vs. machine decision making, and more.
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- Privacy, Security and Ethics in Process Mining - Dec 21, 2016.
Data Privacy, Security and Ethics are hot yet complex topics in the business and data science world. This important article talks about and provide guidelines for privacy, security and ethics, specifically in the context of Process Mining.
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- Cartoon: Scary Big Data - Oct 29, 2016.
What do Halloween and Big Data have in common? Both can be scary, as new KDnuggets cartoon shows.
- Data Science for Internet of Things (IoT): Ten Differences From Traditional Data Science - Sep 26, 2016.
The connected devices (The Internet of Things) generate more than 2.5 quintillion bytes of data daily. All this data will significantly impact business processes and the Data Science for IoT will take increasingly central role. Here we outline 10 main differences between Data Science for IoT and traditional Data Science.
- NYC Taxi Hackathon – find privacy risks in public taxi datasets - Sep 19, 2016.
The NYC TLC has been a pioneer in sharing big data since 2010, but earlier data releases have been de-anonymized. TLC is considering releasing taxi data again, subject to a new anonymization method. This hackathon is to help test it.
- Data Science Challenges - Aug 17, 2016.
This post is thoughts for a talk given at the UN Global Pulse lab in Kampala, and covers the challenges in data science.
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- Big Data Will Rule Your Home - Mar 18, 2016.
The "connected home" is the next frontier for Big Data, and soon our lives may be significantly impacted by the analytical firepower from the IoT. Would benefits outweigh the risks and how would you then feel if your fridge locks you out because your scales and wearables have sounded the warning signs?
- Nurture by Numbers – Big Data and Children - Mar 5, 2016.
Driven by rising healthcare costs and competitions for top schools, more organisations and individuals are turning to Big Data and Analytics to try and give their children the upper hand.
- KDnuggets™ News 16:n06, Feb 17: 21 Must-Know Data Science Interview Q&A; Top Influencers and Brands - Feb 17, 2016.
21 Must-Know Data Science Interview Questions and Answers; Big Data 2016: Top Influencers and Brands; Gartner 2016 Magic Quadrant for Advanced Analytics Platforms: : gainers and losers; Scikit Flow: Easy Deep Learning with TensorFlow and ...
- Privacy – what is it? - Feb 16, 2016.
Bothered about the “big brother” knowing everything about you? We are explaining what exactly the privacy means in this data driven world, what are the different types, the major concerns and its limitation.
- Can Big Data Catch the Bad Guys? - Dec 2, 2015.
Balancing individual liberties with Big Brother surveillance and intelligence-gathering methods means walking a fine line that will require proper balancing for the foreseeable future. Regardless of opinion, Big Data has some role to play in keeping us safe, and the sooner we recognize it the better.
- Online Privacy – Why the Odds are Against You? - Nov 4, 2015.
Infographic on Data Brokers explains how personal information is collected and sold, leaving people with few options to opt-out of it.
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- Can Data Mining Extract Value from your Personal Data (and should you get a piece of the action?) - Sep 2, 2015.
There are industries made out of the personal data, but content providers don’t get their share. What if you are presented with the opportunity to sell your personal data for financial benefits? Find out more by taking the survey from datamilk.
- Cartoon: Big Data and the dog question - Aug 3, 2015.
It used to be that nobody on the internet knew that I was a dog ... New KDnuggets cartoon examines the dog question in the era of Big Data.
- Big Data Big Impact on the Future of Advertising - Jun 26, 2015.
Big data is ready to tack advertisement industry to a new height. Here, we captured how big data will shape the advertising in the future, its challenges and opportunities.
- Should Data Science Really Do That? - May 13, 2015.
Data Science amazing progress in its ability to do predictions and analysis is raising important ethical questions, such as should that data be collected? Should the collected data be used for that application? Should you be involved?
- Be Smarter Than Your Devices: Learn About Big Data - Apr 7, 2015.
If the Apple Watch rollout proves anything, it might be this: Going forward, we’ll all have to be as smart about data as our devices. Also, learn about the origins of "Big Data" term.
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- Hazy Forecast for Consumer Privacy in the Next Decade - Apr 2, 2015.
Majority of experts felt that developing a privacy framework that would be both popular and functional was next to impossible in the near future. With time, privacy is likely tol become a class issue with consumers who have the money having the ability to secure their data better.
- The Internet of People: 4 key principles for analyzing personal data - Feb 27, 2015.
The “Internet of People” raises a host of legal and ethical questions about the ownership and use of our personal data. We all have a stake in joining and navigating this increasingly stormy debate. Here are 4 key principles.
- Beagli: Finding value in your personal data - Feb 25, 2015.
Personal analytics products can help users extract value from their data. This post describes our development of Beagli, a platform for mining and auctioning personal data.
- Big Data, Privacy, and Security – which side are you on? - Feb 18, 2015.
After all the positive promise, the hype, and predictions about Big Data, 2015 started with a debate about privacy and specifically whether or not companies like Google and Facebook should be allowed to encrypt their users data.
- Big Data Could Revolutionize Healthcare. Will We Let it? - Jan 31, 2015.
The power to access and analyze enormous data sets can improve our ability to anticipate and treat illnesses. The benefits for society are just too great, and they won’t be ignored for long.
- KDnuggets™ News 15:n02, Jan 14: Research Leaders on key trends, top papers; Exclusive NYTimes interview - Jan 14, 2015.
Research Leaders on Data Mining key trends, top papers; Exclusive Interview with NYTimes Chief Data Scientist; Majority thinks AI not a threat; Cartoon: Hello, Singularity; Deep Learning in a Nutshell; How to make Privacy and Data Mining Compatible; ClearStory Data CEO on Collaborative Storyboards.
- Exclusive: Interview with Chris Wiggins, NYTimes Chief Data Scientist - Jan 13, 2015.
New York Times Chief Data Scientist Chris Wiggins on the transformation of digital journalism, key Data Science skills, favorite tools, why better wrong than nice, and how Thomas Jefferson is very relevant today.
- Top stories for Jan 4-10: 11 Clever Methods of Overfitting; Research Leaders on Data Science and Big Data - Jan 11, 2015.
11 Clever Methods of Overfitting and how to avoid them; Causation vs Correlation: Visualization, Statistics, and Intuition; Research Leaders on Data Science and Big Data key trends, top papers; Differential Privacy: How to make Privacy and Data Mining Compatible.
- IIA 2015 Analytics Predictions - Dec 11, 2014.
Highlights and discussion from IIA 2015 Analytics Predictions webinar, including Storytelling will be the hot new job in analytics; companies double investment in generating NEW and UNIQUE data, and how does one become an expert if entry-level work is automated?
- Big Data Comic Explains the Current State of Privacy - Nov 7, 2014.
A new comic from Al Jazeera simplifies the Big Data concepts and educates on the privacy concerns based on recent technology advancements.
- Cartoon: Halloween Costume for Big Data - Oct 28, 2014.
New KDnuggets cartoon looks at the appropriate Halloween costume for Big Data and its companion, No Privacy.
- KDD-2014 report, part 2: The Magic Module network and Privacy vs Big Data - Sep 2, 2014.
Here is part 2 of my report on KDD-2014, the biggest and the best Data Science meeting: The Magic Module genes, Privacy vs Big Data, and should we ask for consent of data subjects?
- Interview: Michael Berthold, KNIME Founder, on Research, Creativity, Big Data, and Privacy, Part 2 - Aug 12, 2014.
We discuss interesting research projects, scientific research and creativity, Big Data hype and reality, is privacy still possible, and advice for beginning Data Scientists.
- Future of Consumer Intelligence 2014: Day 2 Highlights - Jul 28, 2014.
Highlights from the presentations by Market Research leaders on day 2 of Future of Consumer Intelligence 2014 in Los Angeles.
- Interview: Dave Marvit, Innovation Strategy Consultant, Fujitsu on Privacy and Sentiment Analysis challenges - Jul 9, 2014.
We discuss the modern sentiment analysis challenges, how to address privacy concerns, Big Data predictions and more.
- Big Data Innovation Summit 2014 London: Highlights - May 31, 2014.
Highlights from the presentations by Big Data technology practitioners from Sears Holdings, Microsoft, Ticketmaster during Big Data Innovation Summit 2014 in London.
- Saxon Global, fast growing BI, Big Data, Cloud Service Provider - May 24, 2014.
Why India is emerging as a powerhouse of Analytics, Big Data Applications, Privacy, What will replace Big Data, and more.
- Media Industry Embracing Analytics for Innovation and Competitive Edge - May 13, 2014.
Survey results highlight the importance of Analytics capability in media industry and the consumer beliefs on privacy vs. personalization benefits.
- Exclusive Interview: David Stringfellow, Chief Economist, State Utah Auditor - Apr 25, 2014.
We discuss Analytics for Public Policy decisions, responsibilities of Utah Chief Data Officer, crowdsourcing analytics for resolving Government problems and most important skills for data science practitioners.
- Are Big Data and Privacy at odds? FICO Interview - Apr 23, 2014.
We discuss privacy, FICO scores, balancing predictive power and non-discrimination, whether technology bringing big data and privacy closer, and most important privacy issues for FICO.
- HP Perspective on Big Data and Analytics: Interview with Mazhar Hussain - Apr 3, 2014.
KDnuggets talks with Mazhar Hussain, HP Big Data & Analytics Services Leader, on key topics for the industry and 4 next big areas in Big Data.
- KDnuggets 14:n08, Is Data Scientist the right career path for you? FiveThirtyEight stumbles - Apr 3, 2014.
Latest analytics/data mining news, including candid advice on Data Scientist career, FiveThirtyEight stumbles on climate change, Boston Panel on Next Big Thing in Big Data, and White House/MIT Big Data Privacy Workshop report.
- Boston AnalyticsWeek Panel Highlights: Next Big Thing in Big Data - Mar 27, 2014.
Boston AnalyticsWeek opens with a vigorous panel discussion, which debates the next "Big Thing" in #BigData, Replacing data scientists by an algorithm, and is Privacy a big obstacle to Big Data?
- Top KDnuggets tweets, Mar 17-18: NSA metadata can find medical/financial conditions; Machine Learning in 7 Pictures - Mar 19, 2014.
Stanford students show NSA metadata can find medical, financial conditions; Machine Learning in 7 Pictures ; Social Networks are investing big in Artificial Intelligence; 7 Key Skills of Effective Data Scientists.