New insights into clean analytics

There is a giant problem with the “collect it all” status quo that pervades on the Internet, this has been clear for a long time. Tracking people has become so widespread that organizations, communities, projects and university labs have sprung up dedicated to detecting and publicizing their presence. Data and analytics are clearly useful for software creators and funders, but they also easily lead to harming people’s privacy and well-being. [Read More]

Usability: the wonderful, powerful idea that betrayed us

Usability triggered a revolution in computing, taking arcane number crunching machines and making them essential tools in so many human endeavors, even those that have little to do with mathematics. It turned the traditional design approach on its head. Initially, experts first built a system then trained users to follow it. User experience design starts with goals, observes how people actually think and act in the relevant context, then designs around those observations, and tests with users to ensure it fits the users’ understanding. [Read More]

Clean Insights: February 2021 Update on Privacy-Preserving Measurement

Greetings, all. I hope this finds you healthy and well, finding ways to enjoy the season (whichever it may be). While everyday still provides new challenges in the life of our team at Guardian Project, we continue to strive to be productive as productive as we can be in our professional and personal lives. I’ve just posted an updated presentation on Clean Insights, reflecting on the symposium in May, and the work we have done since then. [Read More]

New Data Sources: API Key Identifiers and BroadcastReceiver Declarations

A central focus of the Tracking the Trackers project has been to find simple ways to detect whether a given Android APK app file contains code which tracks the user. The ideal scenario is a simple program that can scan the APK and tell a non-technical user whether it contains trackers, but as decades of experience with anti-virus and malware scanners have clearly demonstrated, scanners will always contain a large degree of approximation and guesswork. [Read More]

εxodus ETIP: The Canonical Database for Tracking Trackers

There is a new story to add to the list of horrors of Surveillance Capitalism: the United States’ Military is purchasing tracking and location data from companies that track many millions of people. We believe the best solution starts with making people aware of the problem, with tools like Exodus Privacy. Then they must have real options for stepping out of “big tech”, where tracking dominates. F-Droid provides Android apps that are reviewed for tracking and other “anti-features”, and F-Droid is built into mobile platforms like CalyxOS that are free of proprietary, big tech software. [Read More]

Easy translation workflows and the risks of translating in the cloud

Crowdsourced translation has opened up software and websites to whole new languages, regions, and uses. Making translating easier has brought in more contributors, and deploying those languages requires less work. A number of providers now offer “live”, integrated translation, speeding up the process of delivering translated websites. On the surface, this looks like a big win. Unfortunately, the way such services have been implemented opens up a big can of worms. [Read More]

On the classification of tracking

This position paper tries to outline a framework for defining trackers in smart phones and lists mechanisms for identifying them. It hopes to serve as the foundation for the work done in the Tracking-the-Trackers project. In section 1 we start with an abstract analysis of levels of unwanted behaviour in the context of tracking. Next, in section 2, we focus on an attacker’s perspective, on anonymity and pseudonymity. This foundation allows us to define terms which are needed throughout the paper. [Read More]

"Features" for Finding Trackers

One key component of the Tracking the Trackers project is building a machine learning (ML) tool to aide humans to find tracking in Android apps. One of the most important pieces of developing a machine learning tool is figuring out which “features” should be fed to the machine learning algorithms. In this context, features are constrained data sets derived from the whole data set. In our case, the whole data set is terabytes of APKs. [Read More]

The Promise and Hazards of COVID Contact Tracing Apps

There has been increasing interest in the possibilities of tracking people who are infected with Coronavirus using all of the various methods that smart phones provide. There is good reason: “contact tracing” has been a pillar of public health efforts for decades. It is an effective means to curtail the spread of infectious disease. At the same time, governments, companies, and organizations are acting fast to offer services to help end this current pandemic. [Read More]

Tracking the Trackers: using machine learning to aid ethical decisions

F-Droid is a free software community app store that has been working since 2010 to make all forms of tracking and advertising visible to users. It has become the trusted name for privacy in Android, and app developers who sell based on privacy make the extra effort to get their apps included in the F-Droid.org collection. These include Nextcloud, Tor Browser, TAZ.de, and Tutanota. Auditing apps for tracking is labor intensive and error prone, yet ever more in demand. [Read More]

Tracking usage without tracking people

One thing that has become very clear over the past years is that there is a lot of value in data about people. Of course, the most well known examples these days are advertising and spy agencies, but tracking data is useful for many more things. For example, when trying to build software that is intuitive and easy to use, having real data about how people are using the software can make a massive difference when developers and designers are working on improving their software. [Read More]

How can we learn without watching?

What kind of measurement, tracking or analytics do you use, and can you sleep at night with your decision? As part of the Berkman-Klein Assembly program at Harvard, I am working with a team to imagine a next-generation mobile and IoT analytics system that has privacy, confidentiality and anonymity at its core. The hope is we can find ways to learn what our users like and understand how our apps are performing without having to rely on proprietary cloud services, logging liability, network vulnerabilities, and invasive app permissions. [Read More]