Hype for the Future 103F: Differential Privacy Defined

https://novatopflex.wordpress.com/2026/02/11/hype-for-the-future-103f-differential-privacy-defined/

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

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