All posts
- Winning at DP VISION — suspiciously easily… —
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I crafted a strategy to beat the high score at a game based on differential privacy. It should have been harder to beat, though…
- Paper highlights: Utility-boosting geometric tricks —
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A quick look at three papers who use neat tricks to boost the utility of simple DP operations.
- What's up with all these large privacy budgets? —
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Many real-world DP deployments use privacy parameters that can seem unconvincing. Should we be worried?
- Empirical privacy metrics: the bad, the ugly… and the good, maybe? —
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This post contains the slides and transcript of a talk about empirical privacy metrics that I delivered at PEPR in June 2024.
- Converters between differential privacy variants —
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A small collection of interactive converters between differential privacy variants.
- Paper highlight: Evaluations of Machine Learning Privacy Defenses are Misleading —
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A quick look at a new paper poking at empirical privacy metrics for ML models.
- Five stages of accepting provably robust anonymization —
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This post contains the slides and transcript for an invited talk I delivered at AnoSiDat in April 2024.
- Choosing things privately with the exponential mechanism —
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A simple introduction to an essential building block for differential privacy: how to select a value among many.
- Mapping privacy-enhancing technologies to your use cases —
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A guide listing common privacy-enhancing technologies, and delineating between which problem each one solves.
- What anonymization techniques can you trust? —
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An overview of legacy techniques used to anonymize data, how they fail, and what we can learn from these failures.
- Is differential privacy the right fit for your problem? —
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Some data publication or sharing use cases are well-suited to the use of differential privacy, while some aren’t. In this blog post, we give a litmus test allowing you to quickly distinguish between the two.
- Research post: Differential privacy under partial knowledge —
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What happens to differential privacy if you model a weaker adversary with only partial knowledge over the input data?
- A bottom-up approach to making differential privacy ubiquitous —
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This post contains the slides and speaker notes for an invited talk I delivered at PPAI-22.
- Averaging risk: Rényi DP & zero-concentrated DP —
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Averaging the privacy loss random variable across outputs can be useful: introducing Rényi DP, and zero-concentrated DP.
- A list of real-world uses of differential privacy —
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A list of practical deployments of differential privacy, along with their privacy parameters.
- A friendly, non-technical introduction to differential privacy —
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An introduction and table of contents for my beginner-friendly blog post series about differential privacy.
- Joining Tumult Labs —
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I'm joining Tumult Labs, a startup focused on differential privacy. Here's why I'm excited about it!
- Don't worry, your data's noisy —
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Your data was already noisy before I got a chance to add noise to it! Here's why you shouldn't panic, and also what you should do about it.
- Getting more useful results with differential privacy —
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A few generic pieces of advice on how to get better utility out of your differentially private aggregations.
- Demystifying the US Census Bureau's reconstruction attack —
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The US Census is moving to differential privacy, after running a successful privacy attack on their 2010 release. Let's look at this attack in more detail!
- Why not differential privacy? —
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What does it mean for an algorithm to not be differentially private?
- Converting my PhD thesis into HTML —
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A story of false hopes, perseverance, pain, and futility.
- The magic of Gaussian noise —
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Why is Gaussian noise a popular choice to make statistics and machine learning models differentially private?
- The privacy loss random variable —
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What does \(\delta\) really mean in \((\varepsilon,\delta)\)-differential privacy? Let's explain this using a central concept: the privacy loss random variable.
- A reading list on differential privacy —
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A list of papers to check out to learn more about differential privacy.
- « What does a privacy engineer do, anyway? » —
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Besides having a super cool job title, what is it like being a privacy engineer?
- Local vs. central differential privacy —
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Differential privacy is used in two very distinct contexts. Come learn about the distinction between the two, and interesting new directions that combine them!
- Research post: Cardinality Estimators do not Preserve Privacy —
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You can't both remember unique individuals and not remember them. Shocking, right? :D
- Almost differential privacy —
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Publishing histograms without knowing the categories in advance: introducing (ε,δ)-differential privacy.
- Personal open access policy —
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How I contribute to a healthier model of scientific publishing.
- Differential privacy in practice (easy version) —
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How to add differentially private magic to your statistics, in the easy cases: counts, sums, averages, histograms…
- « So, how does your part-time PhD arrangement actually work? » —
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I got this question many times. So I thought I'd answer it, along with other frequent questions about this arrangement.
- Differential privacy in (a bit) more detail —
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Why does differential privacy work so well? Let's look at it more closely.
- Why differential privacy is awesome —
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A high-level, non-technical explanation of differential privacy and its advantages.
- δ-presence, for when being in the dataset is sensitive —
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δ-presence is a privacy notion which captures a different attack model than what we've previously seen. Let's understand why yet another definition is necessary, and what the solution looks like!
- l-diversity, because reidentification doesn't tell the whole story —
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l-diversity is the first famous attempt at considering stronger attack models than simply reidentification attacks. Let's see how it works, and which flaws of k-anonmyity it fixes!
- Book review: Crash Override —
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A short review of Crash Override, by Zoë Quinn. tl;dr: you should read it, especially if you're building tech products or working in tech policy.
- k-map, the weird cousin of k-anonymity —
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Weakening k-anonymity, really? This sounds weird, but this can actually be quite reasonable. Let's learn why!
- Book review: Twitter and Tear Gas —
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A short review of Twitter and Tear Gas: The Power and Fragility of Networked Protest, by Zeynep Tufekci. tl;dr: you should read it, especially if you participate in activist movements.
- Biometrics: authentication or identification? —
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Know the difference. It probably won't save your life, but it can certainly avoid you saying nonsensical things on the Internet.
- k-anonymity, the parent of all privacy definitions —
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How a privacy researcher proved a politician wrong, and how she created the first ever definition of anonymity in the process.
- Beginnings —
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Blog intro. What's going to be there?