Proof partial. Core topic fields are ready, but questions or supporting reports are still catching up.
The field of privacy and security is advancing with frameworks like Proteus, which enhances device logging without exposing personally identifiable information, and PenTiDef, designed for decentralized federated intrusion detection systems to counteract poisoning attacks. These innovations are crucial as they address the growing concerns around data privacy in digital environments, particularly with the rise of generative AI and machine learning. By ensuring that sensitive data remains protected during analysis and model training, these frameworks empower builders to develop more secure applications while maintaining user trust. Additionally, the introduction of privacy threat modeling for generative AI highlights the need for comprehensive security measures in emerging technologies. Collectively, these efforts are pivotal in shaping a safer digital landscape, enabling builders to innovate responsibly.
Topic-specific paper and score movement from the daily diff ledger.
Device logs are essential for forensic investigations, enterprise monitoring, and fraud detection; however, they often leak personally identifiable information (PII) when exported for third-party anal...
As generative AI (GenAI) systems become increasingly prevalent across various technological stacks, the question of how such systems handle sensitive and personal data flows becomes increasingly impor...
Membership inference attacks (MIAs) have become the standard tool for evaluating privacy leakage in machine learning (ML). Among them, the Likelihood-Ratio Attack (LiRA) is widely regarded as the stat...
Vision-language models are increasingly applied to sensitive domains such as medical imaging and personal photographs, yet existing differentially private methods for in-context learning are limited t...
The increasing deployment of Federated Learning (FL) in Intrusion Detection Systems (IDS) introduces new challenges related to data privacy, centralized coordination, and susceptibility to poisoning a...
We show that large language models can be used to perform at-scale deanonymization. With full Internet access, our agent can re-identify Hacker News users and Anthropic Interviewer participants at hig...
Freshness
Canonical route: /topics
Agent Handoff
Canonical ID privacy-security | Route /topic/privacy-security
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/topic/privacy-securityMCP example
{
"tool": "search_papers",
"arguments": {
"query": "Privacy & Security",
"cluster": "Privacy & Security"
}
}source_context
{
"surface": "topic",
"mode": "topic",
"query": "Privacy & Security",
"normalized_query": "privacy-security",
"route": "/topic/privacy-security",
"paper_ref": null,
"topic_slug": "privacy-security",
"benchmark_ref": null,
"dataset_ref": null
}Use This Via API or MCP
Topic pages bundle paper counts, viability trends, author concentration, and top questions into one canonical surface your agents can reference before they open Signal Canvas or create a workspace.