Making LLMs fast with KV-cache sharing is great. A new paper reports it's also a huge privacy risk.
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Making LLMs fast with KV-cache sharing is great. A new paper reports it's also a huge privacy risk.
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That's why we're excited to see the "SafeKV" paper from researchers at the University of Connecticut, Peking University, and others. Their solution-oriented framework selectively shares non-sensitive data while isolating PII. To validate the "Safe" part of their system, they needed a robust, multilingual privacy benchmark.
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That's why we're excited to see the "SafeKV" paper from researchers at the University of Connecticut, Peking University, and others. Their solution-oriented framework selectively shares non-sensitive data while isolating PII. To validate the "Safe" part of their system, they needed a robust, multilingual privacy benchmark.
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We're proud that the Ai4Privacy pii-masking dataset was used for this critical evaluation related to privacy.
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We're proud that the Ai4Privacy pii-masking dataset was used for this critical evaluation related to privacy.
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This is a perfect win-win. Our open-source data enables researchers to build and validate more effective security solutions for core AI infrastructure. Their work, in turn, helps make the entire LLM ecosystem safer, showing that performance and privacy don't have to be mutually exclusive.
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This is a perfect win-win. Our open-source data enables researchers to build and validate more effective security solutions for core AI infrastructure. Their work, in turn, helps make the entire LLM ecosystem safer, showing that performance and privacy don't have to be mutually exclusive.
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Kudos to Kexin Chu, Zecheng Lin, Dawei Xiang, 沈子旭, Jianchang Su, cheng chu, Yiwei Yang, Wenhui Zhang, Wenfei Wu, and Wei Zhang on this beautiful work.
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Kudos to Kexin Chu, Zecheng Lin, Dawei Xiang, 沈子旭, Jianchang Su, cheng chu, Yiwei Yang, Wenhui Zhang, Wenfei Wu, and Wei Zhang on this beautiful work.
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🔗 Check out their paper to see the future of secure, high-performance LLM inference: 
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🔗 Check out their paper to see the future of secure, high-performance LLM inference: 
https://arxiv.org/pdf/2508.08438
https://arxiv.org/pdf/2508.08438
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#OpenSource
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#OpenSource
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#DataPrivacy
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#DataPrivacy
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#LLM 
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#LLM 
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#Anonymization
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#Anonymization
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#AIsecurity
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#AIsecurity
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#HuggingFace
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#HuggingFace
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#Ai4Privacy 
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#Ai4Privacy 
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#Worldslargestopensourceprivacymaskingdataset
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#Worldslargestopensourceprivacymaskingdataset
Making LLMs fast with KV-cache sharing is great. A new paper reports it's also a huge privacy risk.

That's why we're excited to see the "SafeKV" paper from researchers at the University of Connecticut, Peking University, and others. Their solution-oriented framework selectively shares non-sensitive data while isolating PII. To validate the "Safe" part of their system, they needed a robust, multilingual privacy benchmark.

We're proud that the Ai4Privacy pii-masking dataset was used for this critical evaluation related to privacy.

This is a perfect win-win. Our open-source data enables researchers to build and validate more effective security solutions for core AI infrastructure. Their work, in turn, helps make the entire LLM ecosystem safer, showing that performance and privacy don't have to be mutually exclusive.

Kudos to Kexin Chu, Zecheng Lin, Dawei Xiang, 沈子旭, Jianchang Su, cheng chu, Yiwei Yang, Wenhui Zhang, Wenfei Wu, and Wei Zhang on this beautiful work.

🔗 Check out their paper to see the future of secure, high-performance LLM inference: https://arxiv.org/pdf/2508.08438

#OpenSource
#DataPrivacy
#LLM 
#Anonymization
#AIsecurity
#HuggingFace
#Ai4Privacy 
#Worldslargestopensourceprivacymaskingdataset
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