Many online decision platforms, from hiring marketplaces to auctions, face a tension between efficient decision-making and the protection of participants' privacy. Personal information, such as a candidate's test score or a bidder's valuation linked to protected data, is sensitive, and fear of data resale or reputational harm can make participants reluctant to share it. Furthermore, platforms can be untrusted or even incentivized to resell data, making local privacy guarantees that do not rely on a trusted centralized curator preferable. We initiate the study of optimal stopping and prophet inequalities under local differential privacy (LDP). Each of $n$ independent arriving values is observed only through reports generated by an $\varepsilon$-LDP mechanism. The decision maker must design the $\varepsilon$-LDP mechanisms, and choose an irrevocable stopping time to maximise the expected selected true value. We characterize the optimal online stopping rule under LDP and show that simple binary mechanisms suffice: an optimal LDP stopping rule can be implemented via a randomized-response-type report and a dynamic-programming threshold rule. We then quantify performance via tight competitive ratios against two benchmarks. Relative to the optimal non-private online policy, we prove a tight worst-case competitive ratio of $e^{\varepsilon}/(n - 1 + e^{\varepsilon})$, interpolating between $1/n$ (full privacy) and $1$ (no privacy). Relative to an LDP prophet, who designs $\varepsilon$-LDP mechanisms but observes the full privatized sequence before deciding what to select, we prove a tight competitive ratio of $(1 + e^{-\varepsilon})/2$, interpolating between $1$ (full privacy) and the classical $1/2$ bound (no privacy). Notably, increasing privacy shrinks the LDP prophet's advantage faster than it degrades online performance, closing the performance gap.

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