For questions related to privacy (user permissions / security techniques, internet tracking system such as Cookies, Web bugs etc.)

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Websites and apps collect a variety of sensitive information from their customers and online visitors. If you collect even one iota of information on your website, you need a privacy policy.


Collecting information about people allows you to make significantly better products, and the more information you collect, the better products you can build.


A site that demonstrates all the data your browser knows about you. The data that is displayed can be accessed by any website without asking you for any permission.


This is a guide to how sites can comply with Do Not Track (DNT). It is based on experience gained helping sites give users control over tracking, using the DNT Consent API, where it is supported, to communicate explicit consent to sites and their third-parties.


If you place your information on a publicly facing resource, should it then be a free-for-all for anyone to do whatever they want with? I mean it's now public domain data, right?


In a move with echoes of the fictional internet giant described in Dave Eggers' The Circle, Google's has begun trawling through billions of personal credit card records, matching them to your browser, location and advertising histories. For example, if you bought a TV offline, Google would match your credit card history to your ad profile (containing your GPS record and your browsing data) to "prove" to the merchant that you did, or didn't, see one of its advertisements.


You may know that most websites have third-party analytics scripts that record which pages you visit and the searches you make. But lately, more and more sites use “session replay” scripts. These scripts record your keystrokes, mouse movements, and scrolling behavior, along with the entire contents of the pages you visit, and send them to third-party servers. Unlike typical analytics services that provide aggregate statistics, these scripts are intended for the recording and playback of individual browsing sessions, as if someone is looking over your shoulder.


On the heels of news about concerns regarding the use of certain fitness technologies that could reveal confidential military troop and base locations, comes an entirely different spectrum of issues to consider before allowing for public or partner consumption of your APIs.


It makes all the more sense to identify and examine possible data protection problems when designing new technology and to incorporate privacy protection into the overall design, instead of having to come up with laborious and time-consuming “patches” later on.


Europe’s imminent privacy overhaul means that we all have to become more diligent about what data we collect, how we collect it, and what we do with it. In our turbulent times, these privacy obligations are about ethics as well as law.


Facebook’s Press Release after the Cambridge Analytica story unfolded is one perfect way to show how the whole company thinks. While it’s clear that Facebook didn’t follow their claim to “Protecting people’s information is the most important thing we do at Facebook.”, they declare it Cambridge Analytica’s fault that they used data that Facebook and their users gave them unnecessarily.


Yet another type of surreptitious data collection by third-party scripts: the exfiltration of personal identifiers from websites through “login with Facebook” and other such social login APIs.


Answers to the three most commonly asked questions about maintaining GDPR-compliant machine learning programs.


‘Very few companies are going to be 100 percent compliant on May 25th’


The Tinder app tracks its users’ locations in order to tell potential matches how far away they are from each other. This enables users to make rational decisions about whether it’s really worth traveling 8 miles to see a 6, 6.5 tops, when they’ve also got a tub of ice cream in the fridge and work the next morning. And this means that Tinder knows exactly where Steve is going. And if you can find the right exploit, soon you will too.


Many healthcare organizations are starting to adopt artificial intelligence (AI) systems to gain deeper insight into operations, patient care, diagnostic imaging, cost savings and so on. However, it can sometimes be daunting to even know where to get started. Many times, you need a clear lighted path to start your journey and embrace AI and machine learning (ML) capabilities rapidly.