arXiv:2604.24326cs.CRcs.AI2026-04中稿 · as a regular paper…

让电力用户自主协商隐私保护程度,提升数据共享透明度与信任。

X-NegoBox: An Explainable Privacy-Budget Negotiation Framework for Secure Peer-to-Peer Energy Data Exchange

  • 基于信任、敏感度等动态调整隐私预算,支持智能谈判
  • 减少隐私泄露,接收率提升,决策过程可解释
  • 适合关注数据隐私的能源互联网参与者

现代能源系统的去中心化使消费者成为产消者,持续与聚合商、同行和市场运营商交换数据。尽管这些数据对点对点交易、需求响应和分布式预测至关重要,却可能暴露敏感家庭行为模式,带来隐私风险。现有数据共享机制依赖固定策略或预设差分隐私预算,难以适应可靠性、数据敏感性及请求目的的变化。导致产消者很少获得请求被接受、拒绝或修改的原因,降低信任与参与意愿。为此,我们提出X-NegoBox,一个可解释的协商框架,实现自适应隐私预算分配与透明决策。每个产消者的数据在本地私有DataBox中管理,原始数据不外泄。传入请求由自主隐私预算协商协议(APBNP)处理,依据信任度、特征敏感性、声明用途、历史行为和风险感知定价,动态确定隐私预算。必要时生成隐私保护型反提议,如降低分辨率或时长。可解释协议层(X-Contract)生成人机可读的决策理由。达成协议后,请求方代码在沙箱中本地执行,仅共享净化后的输出。真实能源市场实验表明,该方案有效降低隐私泄露,提高接受率,并显著增强可解释性。

原文摘要 · Abstract (English)

The decentralization of modern energy systems is transforming consumers into prosumers who continuously exchange data with aggregators, peers, and market operators. While such data is essential for peer-to-peer trading, demand response, and distributed forecasting, it can reveal sensitive household patterns and introduce privacy risks. Existing data sharing mechanisms rely on fixed policies or predefined differential privacy budgets, limiting their ability to adapt to variations in reliability, data sensitivity, and request purpose. As a result, prosumers rarely receive explanations for why a request is accepted, rejected, or modified, reducing trust and participation. To address these limitations, we propose X-NegoBox, an explainable negotiation framework for adaptive privacy budgeting and transparent decision making. Each prosumer data is managed locally within a private DataBox, where raw data remain confined. Incoming requests are processed by an Autonomous Privacy Budget Negotiation Protocol (APBNP), which determines an appropriate privacy budget based on trust, feature sensitivity, declared purpose, historical behavior, and risk-aware pricing. When needed, APBNP generates privacy-preserving counter-offers, such as reduced resolution or duration. An Explainable Agreement Layer (X-Contract) produces human- and machine-readable justifications for each decision. After agreement, requester code executes locally in a sandbox, and only sanitized outputs are shared. Experiments on realistic energy market settings show reduced privacy leakage, higher acceptance rates, and improved interpretability.

隐私计算能源互联网可解释性

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