GDPR requires you to be more thoughtful about the sites and services you build, more transparent about the ways you collect and use data, more considerate of your users, and more thorough in your development and documentation processes. In this article, Heather Burns will explore what you, as a developer, need to know about the new data protection regime. At the end, you’ll understand how the challenges posed by the privacy overhaul will ultimately help to make you a better developer.
GDPR - A Practical Guide For Developers - Bozho's tech blog
You’ve probably heard about GDPR. The new European data protection regulation that applies practically to everyone. Especially if you are working in a big company, it’s most likely that there’sContinue reading
A data minimization model for embedding privacy into software systems
Modern software systems (social networking, banking and shopping applications) are becoming increasingly dependent on our data. These systems need data to provide various economic and social benefits to users as well as businesses. However, the extensive use of personal data in systems poses a threat to user privacy. Therefore, privacy laws expect software systems to practice Data Minimization (DM), to minimize data in software systems. This has put software developers in a dilemma to minimize user data to provide user privacy and maximize user data for enhanced system functionality. Following the design science research approach, in this research we propose and evaluate a methodology that enables developers to make their decisions to minimize user data in software systems through understanding data. The methodology encourage developers to think of the ways they would use data in a system design focusing on the storage and sharing of data. Developers in the three experiments conducted to evaluate the methodology agreed that it enables them to think of the ways they use data in system designs and it helps them to make decisions to minimize using data in a system design. Developers also showed positive intention to use the proposed methodology within system development activities.
User Privacy and Large Language Models: An Analysis of Frontier Developers' Privacy Policies
Hundreds of millions of people now regularly interact with large language models via chatbots. Model developers are eager to acquire new sources of high-quality training data as they race to improve model capabilities and win market share. This paper analyzes the privacy policies of six U.S. frontier AI developers to understand how they use their users' chats to train models. Drawing primarily on the California Consumer Privacy Act, we develop a novel qualitative coding schema that we apply to each developer's relevant privacy policies to compare data collection and use practices across the six companies. We find that all six developers appear to employ their users' chat data to train and improve their models by default, and that some retain this data indefinitely. Developers may collect and train on personal information disclosed in chats, including sensitive information such as biometric and health data, as well as files uploaded by users. Four of the six companies we examined appear to include children's chat data for model training, as well as customer data from other products. On the whole, developers' privacy policies often lack essential information about their practices, highlighting the need for greater transparency and accountability. We address the implications of users' lack of consent for the use of their chat data for model training, data security issues arising from indefinite chat data retention, and training on children's chat data. We conclude by providing recommendations to policymakers and developers to address the data privacy challenges posed by LLM-powered chatbots.
User Privacy and Large Language Models: An Analysis of Frontier Developers' Privacy Policies
Hundreds of millions of people now regularly interact with large language models via chatbots. Model developers are eager to acquire new sources of high-quality training data as they race to improve model capabilities and win market share. This paper analyzes the privacy policies of six U.S. frontier AI developers to understand how they use their users' chats to train models. Drawing primarily on the California Consumer Privacy Act, we develop a novel qualitative coding schema that we apply to each developer's relevant privacy policies to compare data collection and use practices across the six companies. We find that all six developers appear to employ their users' chat data to train and improve their models by default, and that some retain this data indefinitely. Developers may collect and train on personal information disclosed in chats, including sensitive information such as biometric and health data, as well as files uploaded by users. Four of the six companies we examined appear to include children's chat data for model training, as well as customer data from other products. On the whole, developers' privacy policies often lack essential information about their practices, highlighting the need for greater transparency and accountability. We address the implications of users' lack of consent for the use of their chat data for model training, data security issues arising from indefinite chat data retention, and training on children's chat data. We conclude by providing recommendations to policymakers and developers to address the data privacy challenges posed by LLM-powered chatbots.
A Starter Guide to Protecting Your Data From Hackers and Corporations
Hackers. AI data scrapes. Government surveillance. Yeah, thinking about where to start when it comes to protecting your online privacy can be overwhelming. Here’s a simple guide for you—and anyone who claims they have nothing to hide.
Privacy by Design
In view of rapid and dramatic technological change, it is important to take the special requirements of privacy protection into account early on, because new technological systems often contain hidden dangers which are very difficult to overcome after the basic design has been worked out. So 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. This approach is known as “Privacy by Design” (PbD).
Measuring the Privacy Experience
How do you know that a product respects your privacy – other than by wading through the fine print? We’ve created a framework to measure the way people actually experience privacy in tech products.
How the EU can fine US companies for violating GDPR
The EU is set to begin enforcing a new set of data privacy regulations in 2018 that will impact companies on both sides of the Atlantic. Here’s how EU regulators can — and will — come after US companies for data privacy violations. European regulators are getting ready to implement a far stricter data-privacy regulation in early 2018, and both European and U.S. companies are working to get into compliance. Called the General Data Protection Regulation (GDPR), the new rules place heavy fines fo...
GDPR, China and data sovereignty are ultimately wins for Amazon and Google | TechCrunch
The Great Privacy Policy Email Deluge of 2018 may have finally petered out, but we are just starting to build an understanding of who the winners and
Data Minimisation: a Language-Based Approach (Long Version)
Data minimisation is a privacy-enhancing principle considered as one of the pillars of personal data regulations. This principle dictates that personal data collected should be no more than...
Facebook Data Privacy Scandal: A Cheat Sheet - TechRepublic
Read about the saga of Facebook's failures in ensuring privacy for user data, including how it relates to Cambridge Analytica, the GDPR, the Brexit campaign, and the 2016 US presidential election.
Privacy Principles
Privacy is an essential part of the web. This document provides definitions for privacy and related concepts that are applicable worldwide as well as a set of privacy principles that should guide the development of the web as a trustworthy platform. People using the web would benefit from a stronger relationship between technology and policy, and this document is written to work with both.
Privacy Principles
Privacy is an essential part of the web. This document provides definitions for privacy and related concepts that are applicable worldwide as well as a set of privacy principles that should guide the development of the web as a trustworthy platform. People using the web would benefit from a stronger relationship between technology and policy, and this document is written to work with both.
Privacy concerns with Google
Privacy concerns with Google include a wide range of issues and controversies related to collection, use, and sharing of user data across Google products, services, and platforms, including Google Search. Since the mid-2000s, civil liberties organizations and government regulators have criticized and taken action against Google related to Internet privacy issues in its data collection and data retention policies, web tracking practices, cooperation with intelligence agencies and law enforcement agencies, and other activities. These concerns are a frequent theme in criticism of Google.
Opt Out October: Daily Tips to Protect Your Privacy and Security
Trying to take control of your online privacy can feel like a full-time job. But if you break it up into small tasks and take on one project at a time it makes the process of protecting your privacy
