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      <managingEditor>fjvbn2003@gmail.com (Youngju Kim)</managingEditor>
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    <title>Privacy-Preserving ML in 2026 — Federated Learning / Flower / NVIDIA FLARE / Opacus / Zama Concrete / OpenMined PySyft Deep Dive</title>
    <link>https://www.youngju.dev/blog/culture/2026-05-16-privacy-preserving-ml-2026-federated-learning-flower-nvidia-flare-opacus-zama-concrete-openmined-pysyft-deep-dive.en</link>
    <description>The 2026 privacy-preserving machine-learning landscape — Federated Learning (Flower 1.13, NVIDIA FLARE 2.5, TensorFlow Federated, PySyft 0.9), Differential Privacy (Opacus 1.5, OpenDP 0.10, Google DP Library), Homomorphic Encryption (Zama Concrete, Microsoft SEAL, OpenFHE), Secure MPC (CrypTen, MP-SPDZ, EzPC), and TEE (Intel SGX, Apple PCC, AWS Nitro Enclaves). We cover real deployments at Google Gboard, Apple Private Cloud Compute, the BraTS medical consortium, Korean/Japanese banks, and how each maps to GDPR / HIPAA / EU AI Act / K-PIPA / APPI obligations.</description>
    <pubDate>Sat, 16 May 2026 00:00:00 GMT</pubDate>
    <author>fjvbn2003@gmail.com (Youngju Kim)</author>
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    <title>プライバシー保護機械学習 2026 完全ガイド - Federated Learning · Flower · NVIDIA FLARE · Opacus · Zama Concrete · OpenMined PySyft 深掘り</title>
    <link>https://www.youngju.dev/blog/culture/2026-05-16-privacy-preserving-ml-2026-federated-learning-flower-nvidia-flare-opacus-zama-concrete-openmined-pysyft-deep-dive.ja</link>
    <description>2026年のプライバシー保護機械学習(PPML)地形図 - Federated Learning(Flower 1.13、NVIDIA FLARE 2.5、TensorFlow Federated、PySyft 0.9)、Differential Privacy(Opacus 1.5、OpenDP 0.10、Google DP Library)、Homomorphic Encryption(Zama Concrete、Microsoft SEAL、OpenFHE)、Secure MPC(CrypTen、MP-SPDZ、EzPC)、TEE(Intel SGX、Apple PCC、AWS Nitro Enclaves)まで。Google Gboard、Apple Private Cloud Compute、BraTS医療コンソーシアム、韓国・日本の金融機関の実運用事例と、GDPR / HIPAA / EU AI Act / K-PIPA / APPI 規制対応を扱います。</description>
    <pubDate>Sat, 16 May 2026 00:00:00 GMT</pubDate>
    <author>fjvbn2003@gmail.com (Youngju Kim)</author>
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    <title>프라이버시 보존 머신러닝 2026 완벽 가이드 - Federated Learning · Flower · NVIDIA FLARE · Opacus · Zama Concrete · OpenMined PySyft 심층 분석</title>
    <link>https://www.youngju.dev/blog/culture/2026-05-16-privacy-preserving-ml-2026-federated-learning-flower-nvidia-flare-opacus-zama-concrete-openmined-pysyft-deep-dive</link>
    <description>2026년 프라이버시 보존 머신러닝(PPML) 지형 - Federated Learning(Flower 1.13, NVIDIA FLARE 2.5, TensorFlow Federated, PySyft 0.9), Differential Privacy(Opacus 1.5, OpenDP 0.10, Google DP Library), Homomorphic Encryption(Zama Concrete, Microsoft SEAL, OpenFHE), Secure MPC(CrypTen, MP-SPDZ, EzPC), TEE(Intel SGX, Apple PCC, AWS Nitro Enclaves)까지. Google Gboard, Apple Private Cloud Compute, BraTS 의료 컨소시엄, 한국/일본 금융권 실제 배포 사례와 GDPR/HIPAA/EU AI Act/K-PIPA/APPI 규제 대응을 다룹니다.</description>
    <pubDate>Sat, 16 May 2026 00:00:00 GMT</pubDate>
    <author>fjvbn2003@gmail.com (Youngju Kim)</author>
    <category>privacy-preserving-ml</category><category>federated-learning</category><category>flower</category><category>nvidia-flare</category><category>opacus</category><category>differential-privacy</category><category>zama</category><category>homomorphic-encryption</category><category>openmined</category><category>mpc</category><category>2026</category><category>deep-dive</category>
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