Noisegate是一个开源差分隐私网关项目，使AI代理能够在数学保证下安全访问敏感数据，确保即使代理被操纵或恶意使用也无法泄露个人记录[1]。该项目通过三类经典隐私攻击演示验证了其保护效能：差分攻击、成员推断和重新识别均在系统中被击败，并在持续集成中进行回归测试[1]。项目的噪声机制经过验证，手动实现与行业标准库OpenDP在35项噪声尺度检查中精度匹配至1e-9，50万样本分布测试通过（D = 0.0014，p = 0.70）[1]。
在隐私预算管理上，Noisegate采用混合zCDP合成机制，相比传统的朴素预算会计提高了3倍效用：在相同隐私保证下，可支持308个查询而非100个[1]。使用默认参数设置（每个查询ε_q = 0.05，总预算ε = 5，δ = 10⁻⁶）时，对20行患者数据的平均查询误差为±12，对32,561行人口普查数据的平均误差仅为±60或0.2%[1]。系统为每个查询收取固定的per_query_epsilon，预算以用户身份、时期和数据集为键在本地持久化，重新连接不会刷新已消耗的预算[1]。
项目可作为MCP服务器与Claude Desktop集成，为AI代理提供结构化查询工具[1]。然而，开发者声明这是一个非生产级别系统，不应用于真实受管制数据（如受保护健康信息PHI、个人可识别信息PII），并依赖本地可信操作员来管理隐私参数[1]。
An open-source differential-privacy gateway called Noisegate has been introduced to enable AI agents to securely access sensitive data under mathematical guarantees[1]. The project ensures that even if agents are manipulated or used adversarially, individual records cannot be compromised[1].
The system has been validated against three established privacy attacks: differencing, membership inference, and re-identification—all systematically defeated and subjected to regression testing in continuous integration[1]. The project's custom noise mechanism has been cross-verified against the industry-standard OpenDP implementation, matching noise scale calculations to 1e-9 precision across 35 checks and passing distribution tests on 500,000 samples with a Kolmogorov-Smirnov statistic of D = 0.0014 (p = 0.70)[1]. Using composite zCDP synthesis rather than naive budget accounting achieves a threefold utility improvement, supporting 308 queries under the same privacy guarantees compared to 100 queries with conventional methods[1].
Under default parameters (per-query epsilon of 0.05, total budget epsilon of 5, and delta of 10⁻⁶), the system achieves mean errors of ±12 on 20-row patient datasets and ±60 or 0.2% on 32,561-row census datasets[1]. Each query consumes a fixed per-query epsilon allocation, with budgets persisted in a database indexed by identity, time period, and dataset; reconnections do not reset accumulated consumption[1]. The project integrates with Claude Desktop as an MCP server, providing AI agents with structured query tools[1]. The developers emphasize this is not production-ready and should not be deployed on regulated sensitive data such as protected health information or personally identifiable information, as it relies on local trusted operator oversight[1].