474 research outputs found
A Practical Searchable Symmetric Encryption Scheme for Smart Grid Data
Outsourcing data storage to the remote cloud can be an economical solution to
enhance data management in the smart grid ecosystem. To protect the privacy of
data, the utility company may choose to encrypt the data before uploading them
to the cloud. However, while encryption provides confidentiality to data, it
also sacrifices the data owners' ability to query a special segment in their
data. Searchable symmetric encryption is a technology that enables users to
store documents in ciphertext form while keeping the functionality to search
keywords in the documents. However, most state-of-the-art SSE algorithms are
only focusing on general document storage, which may become unsuitable for
smart grid applications. In this paper, we propose a simple, practical SSE
scheme that aims to protect the privacy of data generated in the smart grid.
Our scheme achieves high space complexity with small information disclosure
that was acceptable for practical smart grid application. We also implement a
prototype over the statistical data of advanced meter infrastructure to show
the effectiveness of our approach
Self-prompted Chain-of-Thought on Large Language Models for Open-domain Multi-hop Reasoning
In open-domain question-answering (ODQA), most existing questions require
single-hop reasoning on commonsense. To further extend this task, we officially
introduce open-domain multi-hop reasoning (ODMR) by answering multi-hop
questions with explicit reasoning steps in open-domain setting. Recently, large
language models (LLMs) have found significant utility in facilitating ODQA
without external corpus. Furthermore, chain-of-thought (CoT) prompting boosts
the reasoning capability of LLMs to a greater extent with manual or automated
paradigms. However, existing automated methods lack of quality assurance, while
manual approaches suffer from limited scalability and poor diversity, hindering
the capabilities of LLMs. In this paper, we propose Self-prompted
Chain-of-Thought (SP-CoT), an automated framework to mass-produce high quality
CoTs of LLMs, by LLMs and for LLMs. SP-CoT introduces an automated generation
pipeline of high quality ODMR datasets, an adaptive sampler for in-context CoT
selection and self-prompted inference via in-context learning. Extensive
experiments on four multi-hop question-answering benchmarks show that our
proposed SP-CoT not only significantly surpasses the previous SOTA methods on
large-scale (175B) LLMs, but also nearly doubles the zero-shot performance of
small-scale (13B) LLMs. Further analysis reveals the remarkable capability of
SP-CoT to elicit direct and concise intermediate reasoning steps by recalling
50\% of intermediate answers on MuSiQue-Ans dataset.Comment: Accepted by Findings of EMNLP202
- …