Related papers: Secure Linear Programming Using Privacy-Preserving…
Large language models (LLMs) have demonstrated exceptional capabilities in text understanding and generation, and they are increasingly being utilized across various domains to enhance productivity. However, due to the high costs of…
This paper has been withdrawn by the author.
We present an approach for generating differentially private synthetic text using large language models (LLMs), via private prediction. In the private prediction framework, we only require the output synthetic data to satisfy differential…
This paper has been withdrawn by the author due to a crucial sign error in equation 1
This paper has been withdrawn by the authors.
This paper presents a differentially private algorithm for linear regression learning in a decentralized fashion. Under this algorithm, privacy budget is theoretically derived, in addition to that the solution error is shown to be bounded…
This paper has been withdrawn by the author due to some errors.
This paper has been withdrawn by the author due to a mistake.
This paper has been withdrawn by the authors pending corrections.
This paper has been withdrawn by the author(s), due the final version in math.QA/0604564
This paper has been withdrawn by the author, due a crucial error in the main idea.
This paper has been withdrawn by the author due to an error in the proof of Theorem 6.
To preserve data privacy, multi-party computation (MPC) enables executing Machine Learning (ML) algorithms on private data. However, MPC frameworks do not include optimized operations on sparse data. This absence makes them unsuitable for…
We consider an edge computing scenario where users want to perform a linear computation on local, private data and a network-wide, public matrix. Users offload computations to edge servers located at the edge of the network, but do not want…
This paper has been withdrawn by the author due to errors.
In this paper, we propose two exact distributed algorithms to solve mixed integer linear programming (MILP) problems with multiple agents where data privacy is important for the agents. A key challenge is that, because of the non-convex…
This paper has been withdrawn by the authors.
This paper has been withdrawn because the author no longer believes the firewall argument is correct.
This paper has been withdrawn by the author due to a crucial sign error in equation 1.
The paper has been withdrawn by the author, due to it being fundamentally flawed. The author apologizes for any inconvenience it may have caused.