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Small clinical datasets burn privacy budget fast. In this guide, we train with #DifferentialPrivacy (DP‑SGD) in #PyTorch using #Opacus, tune clipping (C) + noise (σ), and plot AUROC vs ε to choose a defensible point.

Read: https://codelabsacademy.com/en/blog/evaluating-privacy-utility-tradeoffs-small-clinical-datasets-opacus-pytorch?source=mastodon

#HealthcareAI #MachineLearning #PrivacyEngineering

In HealthTech, “remove identifiers” isn’t a DataPrivacy strategy. k-anonymity can reduce singling out in shared tables; differential privacy helps when you publish aggregates or answer many queries.

Deep dive + Python demos: https://codelabsacademy.com/en/blog/k-anonymity-vs-differential-privacy-healthcare?source=mastodon

#DifferentialPrivacy #PrivacyEngineering #DataScience #Cybersecurity

In HealthTech, “remove identifiers” isn’t a DataPrivacy strategy. k-anonymity can reduce singling out in shared tables; differential privacy helps when you publish aggregates or answer many queries.

Deep dive + Python demos: https://codelabsacademy.com/en/blog/k-anonymity-vs-differential-privacy-healthcare?source=mastodon

#DifferentialPrivacy #PrivacyEngineering #DataScience #Cybersecurity

This looks encouraging for privacy-preserving LLMs. While the actual differential privacy guarantees are notoriously difficult to interpret, "no memorisation" is a nice headline. Caveat: there is around 30% performance (utility) gap between the private and non-private models.

https://arxiv.org/abs/2510.15001

#privacy #ai #llm #differentialPrivacy

A graph showing total memorisation rates for 4 LLMs. Gemma 3 1B had an exact memorization rate of less than 0.001% and an approximate memorization rate of 0.01%. Gemma 2B and Gemma 2 2B had higher rates. DP Gemma 2 had both rates at 0.

Building healthcare NLP? This guide shows a HIPAA‑aware de‑identification pipeline for clinical notes in Python: regex + PHI tagging, audit‑ready redaction spans, and production tips (versioning, drift). Also: when #DifferentialPrivacy (DP‑SGD) matters for shared models.

Read the full guide: https://codelabsacademy.com/en/blog/building-hipaa-deidentification-clinical-notes-python?source=mastodon

#Healthcare #DataPrivacy #MLOps

Giờ đây, bạn có thể chạy suy luận LLM cục bộ với bảo đảm quyền riêng tư chính thức! Một gói pip mới đã được phát hành, cho phép bạn sử dụng các mô hình ngôn ngữ lớn (LLM) trên thiết bị của mình với tính năng bảo mật dữ liệu mạnh mẽ thông qua suy luận riêng tư vi phân. Nâng cao quyền riêng tư cho người dùng LLM.
#LLM #Privacy #AI #LocalLLM #DifferentialPrivacy #QuyenRiengTu #MoHinhNgonNgu #BaoMatDuLieu

https://www.reddit.com/r/LocalLLaMA/comments/1puhjqk/now_you_can_run_local_llm_inference_with_

Training on mental health data, but worried about privacy and compliance?
Our new deep dive shows how to use DP‑SGD in PyTorch to add rigorous differential privacy to your models without losing clinical signal.

Read the full article:
https://codelabsacademy.com/en/blog/differential-privacy-mental-health-pytorch-dp-sgd?source=mastodon

#DifferentialPrivacy #PyTorch #HealthcareAI #DataScience #MachineLearning #Bootcamps

Our paper documenting the privacy-preserving histogram estimation used to measure application feature use in the Brave web browser has been published.

Ali Shamsabadi, et al. "Nebula: Efficient, Private and Accurate Histogram Estimation" Proceedings of ACM CCS 2025.

https://dl.acm.org/doi/10.1145/3719027.3744789

#DifferentialPrivacy #Telemetry

"Everyone sharing his or her data to train A.I. is great if we agree with the goals that were given to the A.I. It’s not so great if we don’t agree with these goals; and if the algorithm’s decisions might cost us our jobs, happiness, liberty or even lives.

To safeguard ourselves from collective harm, we need to build institutions and pass laws that give people affected by A.I. algorithms a voice over how those algorithms are designed, and what they aim to achieve. The first step is transparency. Similar to corporate financial reporting requirements, companies and agencies that use A.I. should be required to disclose their objectives and what their algorithms are trying to maximize — whether that’s ad clicks on social media, hiring workers who won’t join unions or total deportation counts.

The second step is participation. The people whose data are used to train the algorithms — and whose lives are shaped by them — should help decide their goals. Like a jury of peers who hear a civil or criminal case and render a verdict together, we might create citizens’ assemblies where a representative randomly chosen set of people deliberates and decides on appropriate goals for algorithms. That could mean workers at a firm deliberating about the use of A.I. at their workplace, or a civic assembly that reviews the objectives of predictive policing tools before government agencies deploy them. These are the kinds of democratic checks that could align A.I. with the public good, not just private power.

The future of A.I. will not be decided by smarter algorithms or faster chips. It will depend on who controls the data — and whose values and interests guide the machines. If we want A.I. that serves the public, the public must decide what it serves."

https://www.nytimes.com/2025/11/02/opinion/ai-privacy.html?unlocked_article_code=1.yU8.8BEa.DltbW_WwVhxN&smid=nytcore-android-share

#AI #Algorithms #Privacy #DifferentialPrivacy #AITraining

📬 US-Regierung will Anonymität der Volkszählung aufheben
#Datenschutz #Netzpolitik #CensusBureau #COUNTAct #Deanonymisierung #differentialprivacy #HowardLutnick #Staatsbürgerschaft #TopDown https://sc.tarnkappe.info/61d622

Republicans want to ban differential privacy in the Census, citing data distortion. But removing it could expose personal info, risking privacy for millions. It’s a battle over how we protect data in the age of AI.

https://www.wired.com/story/republicans-differential-privacy-census-overhaul/
#DataPrivacy #Census #Policy #Ethics #DifferentialPrivacy

The #Republican Plan to Reform the #Census Could Put Everyone’s #Privacy at Risk

A little-known #algorithmic process called “differential privacy” helps keep census data #anonymous. Conservatives want it gone.
#differentialprivacy #algorithms

https://www.wired.com/story/republicans-differential-privacy-census-overhaul/

🔏𝗥𝗲𝘃𝗶𝗲𝘄 𝗗𝗮𝘆 𝗧𝘂𝗲𝘀𝗱𝗮𝘆!📖

Differential Privacy is an increasingly popular, though controversial, technique for protecting confidential data by carefully introducing statistical noise. Even if you are very familiar with #DP, you likely don't know quite as much as Dr. Simson Garfinkel ( @xchatty )

This week, Ben Rothke ( @benrothke ) provides a Hall of Fame recommendation in his review of Garfinkel's new book, 𝘋𝘪𝘧𝘧𝘦𝘳𝘦𝘯𝘵𝘪𝘢𝘭 𝘗𝘳𝘪𝘷𝘢𝘤𝘺, from The MIT Press Essential Knowledge Series. ( @themitpress )

Review👉 https://tinyurl.com/2s3jk4cn

#CybersecurityBooks #CyberCanonHoFCandidate #DifferentialPrivacy #DataPrivacy

CyberCanon Book Review for Differential Privacy

"This work on differential privacy has led to a new open-weight Google model called VaultGemma. The model uses differential privacy to reduce the possibility of memorization, which could change how Google builds privacy into its future AI agents. For now, though, the company's first differential privacy model is an experiment.

VaultGemma is based on the Gemma 2 foundational model, which is a generation behind Google's latest open model family. The team used the scaling laws derived from its initial testing to train VaultGemma with the optimal differential privacy. This model isn't particularly large in the grand scheme, clocking in at just 1 billion parameters. However, Google Research says VaultGemma performs similarly to non-private models of a similar size."

https://arstechnica.com/ai/2025/09/google-releases-vaultgemma-its-first-privacy-preserving-llm/

#AI #GenerativeAI #Google #VaultGemma #Chatbots #LLMs #Privacy #DifferentialPrivacy

An interesting #AI model from Google that could be very useful for #Biomedical & #Health applications, where you need to deal no only with #PII but also #PHI:

"VaultGemma: The world's most capable differentially private #LLM"

https://research.google/blog/vaultgemma-the-worlds-most-capable-differentially-private-llm/

#DifferentialPrivacy

Launching the session was Bao & Bindschaedler's "R+R: Towards Reliable and Generalizable Differentially Private Machine Learning," which scrutinizes 11 DPML techniques for #reproducibility. (https://www.acsac.org/2024/program/final/s114.html) 2/6
#DifferentialPrivacy #ML #MachineLearning

Apple ujawnia trzy kluczowe badania z konferencji o prywatności i sztucznej inteligencji

Apple opublikowało prezentacje z Workshop on Privacy-Preserving Machine Learning (20–21 marca 2025), poświęconego prywatności i bezpieczeństwu w rozwoju AI.

Kilka miesięcy temu Apple zorganizowało warsztaty na temat uczenia maszynowego z zachowaniem prywatności, podczas których przedstawiono prezentacje i dyskusje na temat prywatności, bezpieczeństwa i innych kluczowych obszarów odpowiedzialnego rozwoju uczenia maszynowego. Teraz prezentacje te zostały upublicznione.

Podobnie jak niedawno w przypadku prezentacji z 2024 Workshop on Human-Centered Machine Learning, Apple opublikowało post na swoim blogu Machine Learning Research z kilkoma filmami i długą listą badań i artykułów, które zostały zaprezentowane podczas dwudniowego wydarzenia hybrydowego, które odbyło się w dniach 20-21 marca 2025 roku.

Do trzech najważniejszych prac należy zaliczyć.

Local Pan-Privacy for Federated Analytics – badanie Apple pokazuje, jak chronić prywatność danych nawet wtedy, gdy urządzenie zostanie wielokrotnie skompromitowane. Zastosowano nowe szyfrowane metody pozwalające zbierać statystyki bez ujawniania aktywności użytkownika.

Źródło: 9to5Mac.

Scalable Private Search with Wally – Apple zaprezentowało system wyszukiwania z wykorzystaniem differential privacy. Mechanizm Wally dodaje do zapytania losowe dane, co zapewnia anonimowość użytkowników, a jednocześnie umożliwia obsługę milionów żądań przy niższych kosztach.

Źródło: 9to5Mac.

Differentially Private Synthetic Data via Foundation Model APIs – badanie Microsoftu pokazuje, jak generować syntetyczne dane na podstawie modeli foundation, zachowując wartość danych rzeczywistych, ale bez naruszania prywatności.

Źródło: 9to5Mac.

Łącznie udostępniono 25 publikacji, przygotowanych przez badaczy z Apple, Microsoftu, Google oraz czołowych uczelni (m.in. MIT, UC Berkeley, Carnegie Mellon).

Oto ich pełna lista: