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privacy-preserving machine learning

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AntTech
AntTech
Nov 12, 2024 · Artificial Intelligence

Rhombus: Fast Homomorphic Matrix‑Vector Multiplication for Secure Two‑Party Inference – Paper Overview and Live Presentation

The article introduces the Rhombus protocol, a fast homomorphic matrix‑vector multiplication scheme that reduces ciphertext rotations and achieves O(1) communication complexity, enabling efficient privacy‑preserving two‑party inference, and announces a live streaming session where the first author will discuss its technical details and experimental results.

AI securityRhombus protocolhomomorphic encryption
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Rhombus: Fast Homomorphic Matrix‑Vector Multiplication for Secure Two‑Party Inference – Paper Overview and Live Presentation
AntTech
AntTech
May 12, 2022 · Artificial Intelligence

Privacy-Preserving Cross-Domain Recommendation via Differential Privacy and Subspace Embedding

The article reviews a TheWebConf 2022 paper that introduces a two‑stage framework combining differential‑privacy‑based random subspace publishing (using Johnson‑Lindenstrauss and sparse‑aware transforms) with asymmetric deep models to achieve accurate, privacy‑preserving cross‑domain recommendation, and discusses broader differential‑privacy applications.

Recommendation systemsSubspace Embeddingcross-domain recommendation
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Privacy-Preserving Cross-Domain Recommendation via Differential Privacy and Subspace Embedding
AntTech
AntTech
Jun 2, 2020 · Artificial Intelligence

Privacy-Preserving Machine Learning Workshop at CCS 2020 (Ant Shared Intelligence)

The Ant Shared Intelligence workshop at ACM CCS 2020 invites researchers and practitioners to submit short papers on privacy‑preserving machine learning techniques such as secure multi‑party computation, homomorphic encryption, differential privacy, federated learning, and related applications, with a submission deadline of June 21, 2020.

AI securityCCS2020Federated Learning
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Privacy-Preserving Machine Learning Workshop at CCS 2020 (Ant Shared Intelligence)