As Machine Learning (ML) systems continue to grow, the demand for relevant
and comprehensive datasets becomes imperative. There is limited study on the
challenges of data acquisition due to ad-hoc processes and lack of consistent
methodologies. We first present an investigation of current data marketplaces,
revealing lack of platforms offering detailed information about datasets,
transparent pricing, standardized data formats. With the objective of inciting
participation from the data-centric AI community, we then introduce the DAM
challenge, a benchmark to model the interaction between the data providers and
acquirers. The benchmark was released as a part of DataPerf. Our evaluation of
the submitted strategies underlines the need for effective data acquisition
strategies in ML.

通过对当前数据市场的调查，揭示了缺乏提供有关数据集的详细信息、透明定价和标准化数据格式的平台，进而提出了基于数据提供者和获取者之间互动建模的 DAM 挑战，验证了在机器学习中有效的数据获取策略的需求。

数据采集：面向数据中心的人工智能的新领域

Data Acquisition: A New Frontier in Data-centric AI

Inference centers need more data to have a more comprehensive and beneficial
learning model, and for this purpose, they need to collect data from data
providers. On the other hand, data providers are cautious about delivering
their datasets to inference centers in terms of privacy considerations. In this
paper, by modifying the structure of the autoencoder, we present a method that
manages the utility-privacy trade-off well. To be more precise, the data is
first compressed using the encoder, then confidential and non-confidential
features are separated and uncorrelated using the classifier. The confidential
feature is appropriately combined with noise, and the non-confidential feature
is enhanced, and at the end, data with the original data format is produced by
the decoder. The proposed architecture also allows data providers to set the
level of privacy required for confidential features. The proposed method has
been examined for both image and categorical databases, and the results show a
significant performance improvement compared to previous methods.

本文提出了一种利用自编码器结构来平衡数据提供商和推理中心隐私和效用之间权衡的方法，其中将数据分为私密和非私密部份并通过分类器将其与噪音合并，最后使用解码器将其重构，结果表明该方法比先前的方法具有更好的性能。