THE INTERNET IS THINKING
Earlier sources: Privacy
This security thread follows privacy through original research, public projects, and source reports. Read the evidence below to see which developments are represented in SYMTRI’s archive.
SYMTRI organizes observations from public sources into a living map. Connections reflect shared subject matter; they do not establish causation or endorse a source.
Open in the map ↗116 classified signals in the fourteen-day window. These counts include signals awaiting readable evidence.
Earlier sources
Up to ten sources with readable evidence, ordered by their feed timestamp. These archive sources can be older than the live fourteen-day window. This list is a sample, not a measure of total activity. Dates distinguish paper publication, repository creation, model/dataset activity, Hacker News posts, and KEV catalog additions. “First archived” records when SYMTRI first stored the signal.
- OpenAlex · Published · First archived
Local versus central differential privacy under final-model privacy attacks in low-client-count cross-silo federated learning ↗
Federated learning reduces the need to centralize raw data, but does not prevent privacy leakage from client updates, aggregation messages, or the final released model. This work compares local differential privacy (LDP), central differential privacy (CDP), record-level differentially private stochastic gradient descent (DP-SGD), and secure aggregation (SecAgg) in a low-client-count cross-silo setting with full client participation, across four datasets spanning image classification, tabular binary classification, and two time-series forecasting tasks. The study separates training-time confide…
- GitHub · Repository created · First archived
goosehouse-llc/redde ↗
iPhone, iPad and CarPlay client for the Hermes AI agent or any OpenAI-compatible model. Talk or type, watch it work, approve what it runs. No account, no tracking.
- arXiv · Published · First archived
Revisiting Certified Defense with Differential Privacy on Vision Transformers ↗
Certified defenses that incorporate differential privacy have proven effective on Convolutional Neural Networks (CNNs), furnishing rigorous robustness guarantees against norm-bounded adversaries. However, the certified robustness behavior of Pixel Differential Privacy (PixelDP) remains largely unexplored with the self-attention architecture now dominating the deep-learning landscape. Given that the Transformer has a profound impact on our daily applications from the digital world to the physical world, it is crucial to study certified robustness through differential-privacy-style stability. To…
- GitHub · Repository created · First archived
YSamed/yalqen ↗
A browser that stays out of your way. Vertical tabs, a command bar, and built-in ad & tracker blocking for macOS.
- Hacker News · Posted on Hacker News · First archived
Meta needs you to believe it cares about privacy ↗
Hacker News discussion.
- GitHub · Repository created · First archived
omnesis-dev/Omnesis ↗
Index, search, and reason about your entire digital life.
- GitHub · Repository created · First archived
shrestha-bishal/beforework ↗
Beforework is a serverless, local-first project management workspace. Plan work across multiple workspaces in List, Table, Board, Calendar, Roadmap and Milestone views, with tasks, custom fields, comments, checklists, reminders, attachments, focus sessions, and Google Calendar sync. An alternative to Jira, Linear, Monday, Plane, and ClickUp.
- OpenAlex · Published · First archived
Privacy-Preserving Federated Learning for Building Energy Forecasting: A Differential Privacy Analysis of Aggregation Strategies ↗
Forecasting hourly electricity consumption across distributed building portfolios is critical for grid management and demand response, yet the sensitivity of granular energy data constrains centralised aggregation. Federated learning (FL) trains models in situ, but the cost of layering formal differential privacy (DP) guarantees atop FL remains underexplored for building-energy workloads. We train a CNN-LSTM forecasting model under three FL aggregation strategies (FedAvg, FedProx, FedBN) across four cumulative privacy budgets (ε ∈ {0.5, 1, 3, 6.5}, δ = 10−5) over a federation of 50 commercial…
- GitHub · Repository created · First archived
SysAdminDoc/HushFacebook ↗
Hushfacebook v0.5.0: 51 patches for Facebook 580.0.0.51.74 with Morphe. Hide ads and suggested posts, save videos, tune playback and Reels, and use a focused Marketplace mode.
- Hacker News · Posted on Hacker News · First archived
Privacy: The Sequel (2015) ↗
Hacker News discussion.
Live window recorded . This is the ingestion observation time, not the publication or discovery date of each source.
How to assess evidence about Privacy
Check each original source for its methods, release status, limitations, and supporting data. A repository announcement, a preprint, and a published study provide different kinds of evidence. SYMTRI’s subject classification helps discovery; it does not independently validate the source’s claims.
See the collection and counting methodology ↗ · Read the weekly synthesis ↗
Related threads
Explore related regions
These navigation suggestions are part of the map’s editorial structure, rather than measured evidence of a connection.