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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 ↗115 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.
- Hacker News · Posted on Hacker News · First archived
Map of where crypto and privacy services are sanctioned or blocked ↗
Hacker News discussion.
- GitHub · Repository created · First archived
DanielLetto2020/messhub ↗
One window for all desktop notifications on Linux: a board with a column per app, mail-like rules, search, stats. Stored locally. / Все уведомления рабочего стола в одном окне
- arXiv · Published · First archived
Decoupled Learning and Selection in Slate Recommendation for Privacy and Stability Under Noisy Scores ↗
We formalize slate recommendation as a randomized score learner followed by deterministic selection. First, an appropriately scoped differential-privacy guarantee passes through selection and its audit trace by post-processing. End-to-end privacy holds only when selector inputs are public or independent, previous private outputs, or separately privacy-accounted; fixing raw state or candidate information instead yields only a conditional guarantee. Second, we derive a logged margin certificate: bounded score-induced objective movement below half the smallest greedy decision margin guarantees th…
- GitHub · Repository created · First archived
Morningstar202604/iris ↗
Iris - your personal AI super-assistant. Fast, lightweight, fully yours. Protocol-first (any OpenAI-compatible endpoint), 292+ plugins, knowledge-base RAG, local-first & private. Official site: x33834.github.io/iris
- OpenAlex · Published · First archived
Privacy-Aware Customer Analytics in Physical Stores Using an Edge-Cloud Hybrid Artificial Intelligence System ↗
Customer analytics in physical stores must operate under three simultaneous constraints that are absent or weaker in online retail: limited on-site computation, strict personal-data privacy obligations, and the lack of a natural digital record of customer behavior. This study proposes an end-to-end edge-cloud hybrid artificial intelligence (AI) system that addresses these constraints jointly. On the edge side, an NVIDIA Jetson Nano device equipped with a Sony IMX477 camera performs motion detection, face detection, person detection, person re-identification, and demographic and affective attri…
- arXiv · Published · First archived
Privacy-Preserving Semantic Segmentation from High-Resolution Depth and Ultra-Low-Resolution RGB ↗
As mobile robots become increasingly integrated into everyday environments, privacy risks arising from onboard cameras have become a growing concern. Ultra-low-resolution (ULR) RGB can mitigate visual privacy exposure at the source, but ULR appearance alone substantially limits semantic and spatial understanding. We therefore introduce a privacy-preserving asymmetric sensing setting that combines high-resolution (HR) depth with ULR RGB, preserving dense geometry while restricting fine-grained visual information. To address the severe information imbalance between HR depth and ULR RGB, we propo…
- arXiv · Published · First archived
Contraction and Statistical Inference under Privacy for Uniformly Bounded Distributions ↗
We investigate $c$-interior pointwise maximal leakage (PML) as a tool for contraction analyses and disclosure control. Based on the strong adversarial threat models from maximal leakage, $c$-interior PML generalizes local differential privacy (LDP) to data-generating distributions with densities uniformly bounded away from zero by $c>0$. Viewing $c$-interior PML as an algebraic constraint on a kernel yields more flexible (and often tighter) contraction analyses than standard LDP. We provide tight bounds on the Dobrushin coefficient, and bound the contraction coefficient of the Hockeystick-dive…
- OpenAlex · Published · First archived
An RLWE-Based Privacy-Preserving Key Agreement Method for Offshore Wind Farms ↗
Offshore wind farms are representative unmanned energy systems whose operation relies heavily on remote information exchange between offshore substations and onshore centralized control centers. However, the cross-sea communication link is vulnerable to cyberattacks, including impersonation and message tampering, and these threats persist in the quantum computing era, potentially compromising operational decision-making and control. To address this problem, this paper proposes a privacy-preserving key agreement method based on the Ring Learning With Errors (RLWE) problem. First, identity priva…
- OpenAlex · Published · First archived
Decoupling Network Layer Location and Transport Layer Identity for Communication Flow Privacy ↗
Although the tracking of a user’s network location is often considered a violation of privacy, building technical guarantees of topological location privacy (and geolocation by inference) is difficult: topological location information is encoded in the Internet Protocol (IP) address, and is necessarily exposed both to endpoints and along the communication path, for correct packet forwarding. Existing defences either extend the trust boundary to a third party (e.g., Virtual Private Networks (VPNs)), or operate at the application layer (e.g., Tor), and do not provide a general defence for all pr…
- OpenAlex · Published · First archived
Frequency-guided adaptive privacy masking network ↗
Medical image sharing and collaborative analysis have become essential for advancing diagnostic capabilities across healthcare institutions, yet they introduce substantial privacy risks when sensitive patient information is exposed during transmission or processing. Existing approaches to privacy preservation in medical imaging suffer from fundamental limitations: image-level obfuscation methods inevitably degrade diagnostic utility, cryptographic techniques impose prohibitive computational overhead, and federated learning remains vulnerable to gradient-based inference attacks. This paper prop…
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.
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These navigation suggestions are part of the map’s editorial structure, rather than measured evidence of a connection.