利用公开数据生成更私密的时序数据，提升隐私与实用性的平衡。
Privacy-Aware Time Series Synthesis via Public Knowledge Distillation
- 用自注意力机制融合公开的上下文信息，指导私有时序数据生成。
- 在金融、能源等多领域实验中，隐私-效用权衡优于现有方法。
- 提出可量化合成数据可识别性的新指标，便于实际评估隐私保护能力。
在金融、医疗和能源消费等领域，敏感时序数据（如患者记录或投资账户）因隐私顾虑难以共享。现有隐私保护的数据生成方法通常仅依赖噪声训练，导致隐私与实用性之间的权衡不佳。然而，敏感序列常与公开的非敏感上下文元数据相关（如家庭用电量受天气和电价影响）。本文提出Pub2Priv框架，通过融合异构公开知识生成私有时间序列数据。模型采用自注意力机制将公共数据编码为时序与特征嵌入，作为扩散模型生成合成私有序列的条件输入。此外，我们引入一种实用的隐私评估指标，通过检测合成数据的可识别性来衡量隐私水平。实验结果表明，Pub2Priv在金融、能源及商品交易等多个领域，均持续优于当前最优基准，显著改善了隐私-效用的权衡表现。
原文摘要 · Abstract (English)
Sharing sensitive time series data in domains such as finance, healthcare, and energy consumption, such as patient records or investment accounts, is often restricted due to privacy concerns. Privacy-aware synthetic time series generation addresses this challenge by enforcing noise during training, inherently introducing a trade-off between privacy and utility. In many cases, sensitive sequences is correlated with publicly available, non-sensitive contextual metadata (e.g., household electricity consumption may be influenced by weather conditions and electricity prices). However, existing privacy-aware data generation methods often overlook this opportunity, resulting in suboptimal privacy-utility trade-offs. In this paper, we present Pub2Priv, a novel framework for generating private time series data by leveraging heterogeneous public knowledge. Our model employs a self-attention mechanism to encode public data into temporal and feature embeddings, which serve as conditional inputs for a diffusion model to generate synthetic private sequences. Additionally, we introduce a practical metric to assess privacy by evaluating the identifiability of the synthetic data. Experimental results show that Pub2Priv consistently outperforms state-of-the-art benchmarks in improving the privacy-utility trade-off across finance, energy, and commodity trading domains.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。