Anthropic and OpenAI will watermark their responses.
Yet this violates user privacy and incentivizes users to paraphrase away the watermark. A lose for both sides!
Instead, we rethink the incentive structure, and propose to shift the AI provenance goal from individual tracking to estimating aggregate AI usage.
We develop locally differentially private watermarks which provably limit the evidence that any individual text can contribute to detection, while still enabling accurate estimation of population prevalence of watermarked texts.
Yet this violates user privacy and incentivizes users to paraphrase away the watermark. A lose for both sides!
Instead, we rethink the incentive structure, and propose to shift the AI provenance goal from individual tracking to estimating aggregate AI usage.
We develop locally differentially private watermarks which provably limit the evidence that any individual text can contribute to detection, while still enabling accurate estimation of population prevalence of watermarked texts.
