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SecureLLM: Using Compositionality to Build Provably Secure Language
  Models for Private, Sensitive, and Secret Data

SecureLLM: Using Compositionality to Build Provably Secure Language Models for Private, Sensitive, and Secret Data

16 May 2024
Abdulrahman Alabdulakreem
Christian M Arnold
Yerim Lee
Pieter M Feenstra
Boris Katz
Andrei Barbu
ArXivPDFHTML

Papers citing "SecureLLM: Using Compositionality to Build Provably Secure Language Models for Private, Sensitive, and Secret Data"

4 / 4 papers shown
Title
Bridging Today and the Future of Humanity: AI Safety in 2024 and Beyond
Bridging Today and the Future of Humanity: AI Safety in 2024 and Beyond
Shanshan Han
84
1
0
09 Oct 2024
From Words to Worlds: Compositionality for Cognitive Architectures
From Words to Worlds: Compositionality for Cognitive Architectures
Ruchira Dhar
Anders Sogaard
40
0
0
18 Jul 2024
Differentially Private Fine-tuning of Language Models
Differentially Private Fine-tuning of Language Models
Da Yu
Saurabh Naik
A. Backurs
Sivakanth Gopi
Huseyin A. Inan
...
Y. Lee
Andre Manoel
Lukas Wutschitz
Sergey Yekhanin
Huishuai Zhang
134
346
0
13 Oct 2021
Extracting Training Data from Large Language Models
Extracting Training Data from Large Language Models
Nicholas Carlini
Florian Tramèr
Eric Wallace
Matthew Jagielski
Ariel Herbert-Voss
...
Tom B. Brown
D. Song
Ulfar Erlingsson
Alina Oprea
Colin Raffel
MLAU
SILM
290
1,814
0
14 Dec 2020
1