Encrypted Client
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Related Articles from SNS
Private Embedding Lookup with Encrypted Compact Queries under Fully Homomorphic Encryption
Announce Type: replace Abstract: Many NLP or recommendation models begin by mapping discrete client inputs to embedding vectors. Since inputs can reveal sensitive information, the embedding step must be protected in privacy-preserving inference. Fully Homomorphic Encryption (FHE) enables inference over encrypted client data, but turns embedding lookup from simple table access into homomorphic computation.
Private Embedding Lookup with Encrypted Compact Queries under Fully Homomorphic Encryption
arXiv:2606.03191v2 Announce Type: replace Abstract: Many NLP or recommendation models begin by mapping discrete client inputs to embedding vectors. Since inputs can reveal sensitive information, the embedding step must be protected in privacy-preserving inference. Fully Homomorphic Encryption (FHE) enables inference over encrypted client data, but turns embedding lookup from simple table access into homomorphic computation.
Private Embedding Lookup with Encrypted Compact Queries under Fully Homomorphic Encryption
arXiv:2606.03191v1 Announce Type: new Abstract: Many NLP or recommendation models begin by mapping discrete client inputs to embedding vectors. Since inputs can reveal sensitive information, the embedding step must be protected in privacy-preserving inference. Fully Homomorphic Encryption (FHE) enables inference over encrypted client data, but turns embedding lookup from simple table access into homomorphic computation.
Zeroserve: A zero-config web server you can script with eBPF
zeroserve is a small, fast, zero-config HTTPS server. You hand it a tarball of a website and it serves it - over HTTP/2 and TLS 1.3, with hot reload and a tiny resident footprint. The twist is that you can drop eBPF programs into the tarball and they run on every request, in userspace, as sandboxed middleware - rewriting, authenticating, and rate-limiting requests, or reverse-proxying them to a backend when you want it to act as a gateway in front of your app.
Selective Token-Level Cryptographic Redaction for Privacy-Preserving Clinical Deployment of Large Language Models
Announce Type: new Abstract: While large language models (LLMs) are increasingly used for clinical applications, many existing pipelines require sending raw sensitive health information to remote servers for processing, which heightens the risk of privacy leakage. A natural approach to mitigate this risk is to encrypt the data before transmission. However, straightforward solutions such as encrypting the entire dataset introduce prohibitive computational, alignment, and communication...
Lightweight, Practical Encrypted Face Recognition with GPU Support
arXiv:2604.00546v3 Announce Type: replace Abstract: Face recognition models operate in a client-server setting where a client extracts a compact face embedding and a server performs similarity search over a template database. This raises privacy concerns, as facial data is highly sensitive. To provide cryptographic privacy guarantees, one can use fully homomorphic encryption to perform end-to-end encrypted similarity search.
Privacy-Enhanced Zero-Order Federated Learning via xMK-CKKS over Wireless Channels
arXiv:2605.30123v2 Announce Type: replace Abstract: Homomorphic encryption (HE) enables privacy-preserving aggregation in federated learning (FL) by allowing the server to operate on encrypted data without decryption. Existing HE-over-the-air (OTA) methods mainly rely on single-key HE schemes and require channel estimation or pre-equalization to compensate for wireless fading. However, single-key HE remains vulnerable to honest-but-curious (HBC) clients holding the shared secret key, while...
A Post-Quantum Future for Let's Encrypt
Let’s Encrypt is committed to a post-quantum-safe Web PKI. The path we’re planning to take is Merkle Tree Certificates (“MTCs”), a new approach that adds post-quantum authentication to the web without sacrificing the speed and reliability that have made TLS universal. This post is about these plans and why we believe MTCs are worth pursuing as a key to a post-quantum future.
Exploring CKKS Parameter Trade-offs for Privacy-Preserving Personalized Federated Learning
Announce Type: new Abstract: Privacy-preserving Personalized Federated Learning (PFL) enables clients to collaboratively train personalized models without exposing raw data, but exchanged model updates remain vulnerable to inference attacks from honest-but-curious servers. Homomorphic Encryption (HE) addresses this by allowing server-side aggregation directly on encrypted updates, with the CKKS scheme being particularly suitable due to its native support for approximate floating-point...
Signal says UK plan to scan devices for nude images 'endangers us all'
Signal insists that plans to compel tech companies to scan devices for nude images of children announced by UK Prime Minister Keir Starmer on Monday at London Tech Week "will not keep children safe." "It endangers us all," the encrypted messaging platform said, adding that the mechanism required to implement it would be "dangerous." And it wouldn't be a pro-privacy statement without calling it "dystopian."