STEM与日常科技·英语30篇（5）
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Differential Privacy: Sharing Data Without Exposing People
差分隐私：共享数据而不暴露个人
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Governments and hospitals often publish statistics—like disease rates or income averages—to guide policy.
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But raw data releases risk re-identifying individuals through cross-referencing with other public sources.
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Differential privacy solves this by adding carefully calibrated mathematical noise to each query result.
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For example, when counting infected patients in a town, the system might add ±3 random people.
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This small uncertainty hides any single person’s status while preserving overall trends.
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Crucially, attackers cannot tell whether a specific individual was included in the dataset.
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The privacy guarantee holds even if they know everything else about everyone else.
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Apple and Google use it to collect typing habits and location patterns without storing personal logs.
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Regulators now require differential privacy for many public health and census datasets.
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It proves strong privacy and useful data can coexist—if designed with math, not just good intentions.