A custom Android keyboard can give its developer control over the keyboard’s code and data flows, but it does not make text handling disappear: an input method must process what you type and may read nearby text to support editing. The privacy gain depends on the actual app—its storage, dependencies, logs, and network behavior—not on the fact that it was vibe-coded or custom-built.
What an Android keyboard can access
Android implements a keyboard as an app containing an input-method service, commonly built by extending InputMethodService. The app declares that service and its binding permission in the manifest, and can include a settings activity. It displays an input view and may show a candidate view for suggestions or corrections. Android’s guide to creating an input method describes this structure.
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The keyboard sends text to the focused app through InputConnection. That interface can also retrieve text before or after the cursor, which is useful for editing and composing text. This is a documented capability for input and editing—not unrestricted access to everything in every app. The keyboard’s implementation determines how it handles the text it receives.
Password fields need special care: Android’s guide instructs developers to hide password text in both the input and candidate views and not to store passwords. A keyboard is therefore a sensitive component even if it never sends text over the network.
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Does Gboard collect what you type?
There is no single answer that applies to every Gboard release, data flow, and privacy mechanism. A 2022 dissertation by Kamil Przepiorowski at Trinity College Dublin analyzed network traffic and reverse-engineered telemetry from particular versions of Gboard, SwiftKey, and AnySoftKeyboard. For the Gboard and SwiftKey versions studied, it reported timestamped usage metadata such as the app in which the keyboard was used, input method, language, word length, entry and deletion counts, and timing, with identifiers that could link data to a handset.
The dissertation did not observe Gboard or SwiftKey transmitting typed content or the frequency of individual characters. Its author also said the Gboard reverse engineering was not exhaustive, so this finding is not proof that no other data was collected, nor a current audit of all releases. The study’s results concern the versions and methods examined, not every user or current build. Read the 2022 dissertation.
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Separately, Google researchers described Gboard’s federated-learning approach in a June 2023 technical paper. According to that vendor-authored account, raw training data such as typing content can remain on the device during federated learning; model updates use secure aggregation and differential privacy techniques, and users can configure machine-learning use in Gboard settings. This explains Google’s stated design for those mechanisms. It is not a comprehensive independent audit of all Gboard traffic, telemetry, or current versions. Read the June 2023 paper.
What a custom keyboard changes
Building your own input method changes who controls its code and design decisions. It can be designed to avoid network access, analytics, text logging, or remote dictionary sync if those features are not needed. It does not, by itself, establish that the app works offline, retains no text, or has eliminated telemetry. Those claims require checking the particular build and its components.
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A practical design starts with an inventory of data flows:
- What text does the keyboard read around the cursor, and how long does it remain in memory?
- Does it keep a personal dictionary, and is that dictionary stored only on the device?
- Can logs, diagnostics, or crash reports contain typed text or nearby context?
- Which libraries and SDKs are included, and do any make network requests?
- Does any feature require account identifiers, synchronization, or remote services?
- How are password and other sensitive fields handled in both input and suggestion views?
Android Open Source Project privacy guidance says, “If at all possible, don’t collect metrics.” If metrics are necessary, it recommends explicit, informed, meaningful consent; collecting only what is needed for reliability; avoiding identifiable or potentially sensitive data; and aggregating and anonymizing where possible. It also advises: “Apps or system services shouldn’t log data provided from third-party apps that might include sensitive information.” The page was last updated 2026-09-30 UTC. Read the AOSP privacy best practices.
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How to assess a keyboard’s privacy claims
Source code, privacy disclosures, and network observations can each provide evidence, but they answer different questions. Reviewing code does not by itself prove what a released build does; observing no traffic in a test does not prove that a different configuration or later session sends none. For a particular keyboard build, check the following:
- Inspect the manifest. Review declared permissions, input-method services, and other components to understand the app’s requested capabilities and configuration.
- Review dependencies and SDKs. Identify included libraries that could add analytics, diagnostics, advertising, or other network behavior.
- Search the code. Look for logging, analytics, crash reporting, dictionary synchronization, and network calls, then trace what data those paths handle.
- Review local retention. Check whether typed text or cursor context is saved, how a dictionary is stored, and whether sensitive fields are excluded.
- Read the privacy disclosure. Compare stated collection and sharing practices with the code and the app’s actual features.
- Observe traffic on the specific build and device. Network capture can show observed connections under the tested conditions; it cannot establish that no data is ever collected in every configuration.
Can you trust an open-source keyboard?
Open source makes code available for inspection, but the label alone does not establish how a particular release behaves. A 2022 study reported that the AnySoftKeyboard version it examined showed no observed telemetry collection or sharing. Przepiorowski’s dissertation concluded, “AnySoftKeyboard, the open-source alternative which was studied, does not collect or share any data at all.” That is the dissertation author’s finding about the studied version and method, not a guarantee from the developers about later releases.
When comparing keyboards, look beyond whether their source is public. Consider whether text itself leaves the device, whether metadata or identifiers are sent, whether core features work offline, and what prediction and autocorrect quality, language and layout support, password handling, update cadence, and reproducible source or builds are available. Privacy claims are strongest when they are specific to a version and supported by evidence about the shipped app.
What vibe-coding a keyboard does—and does not—prove
A quickly generated or custom-written keyboard can be a useful way to take control of implementation choices. It is not, on its own, evidence that a keyboard is private, secure, reliable, or feature-complete. The developer still needs to understand the input-method service, protect sensitive fields, audit dependencies and data retention, and verify network behavior on the build people will install. The meaningful claim is not “I made it, so it cannot collect data,” but “this specific implementation has these data flows, and here is how they were checked.”
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