Abstract

Abstract

Ask an app for the weather or the nearest pharmacy, and your phone already knows where you stand to within a few metres. The service needs only your city. Yet once an app is allowed to read precise location, that exact coordinate can leave the device through several routes: the app itself, an embedded analytics or advertising SDK, a background cloud sync, or an AI agent’s tool call. These often reach destinations the user never saw as separate disclosures. Repeated, individually harmless coordinates can reveal a home, a workplace, a clinic visit, a daily routine or activity around sensitive sites, and AI makes that inference cheap and continuous. Operating-system permissions answer whether an app may read location. They rarely decide what precision may leave, to whom, for what purpose and under which jurisdiction.

This disclosure moves that decision to the point of egress. A proposed outbound location release is staged as a non-effective Location-Release Candidate Act. A Protected Enforcement Domain evaluates the request against:

  • the requesting app and component (first-party code, SDK, analytics, advertising or AI agent)
  • declared purpose, recipient, destination and jurisdiction
  • data class and fields, and whether use is one-shot or continuous
  • user authorisation, and policy and revocation epochs
  • cumulative disclosure state

It then selects a precision ceiling on a ladder from EXACT through METER_10, METER_100, GRID, GEOHASH, CITY, REGION and COUNTRY down to NONE, with delayed or randomised variants. It issues scoped, non-bearer, signed authority bound to that ceiling, field set, destination and sink.

An independent egress Finality Sink then inspects the actual outbound payload, not the declared label. It refuses release if a finer value appears anywhere, including hidden JSON fields, headers, query strings, binary or compressed encodings, split values or media metadata. Unknown encodings fail closed. A protected accumulator can downgrade, delay, randomise or deny releases that would together form a movement history.

The disclosure includes JSON interoperability objects, failure codes, a threat model covering compromised SDKs and AI-agent exfiltration, and a Python reference implementation (98 tests; about 0.5 ms mean local decision time in one test environment). Precise GPS access is not precise GPS-release authority.

Industry relevance (AI-generated): the architecture concerns publicly described location ecosystems, including:

  • Apple iOS, Safari and Apple Maps
  • Google Android, Chrome, Maps and Gemini
  • Samsung and other Android device makers
  • Microsoft Windows, Edge and Copilot
  • mapping and location platforms such as HERE and Mapbox
  • advertising, attribution and analytics SDK vendors
  • AI assistant providers whose agents use location context
  • telecom operators

These are named only as examples of where location leaves devices. Naming them does not allege any leakage, deficiency, non-compliance or infringement, and does not imply endorsement. Regulatory context includes GDPR data minimisation and protection by default, ePrivacy location rules, US state laws treating precise geolocation as sensitive, enforcement against location-data trading, India’s DPDP Act and the EU AI Act. This industry-relevance material is AI-generated and must be cross-checked against the original PCT filings and primary sources before drawing any conclusion.

This technical disclosure is based on the Mothership application PCT/IB2026/055615, filed 4 June 2026 and publicly disclosed 16 July 2026 (WO 2026/150382). That application claims priority from PCT/IB2026/053385, filed 7 April 2026 and publicly disclosed 4 June 2026 (WO 2026/115520). Related family applications named in the source draft are PCT/IB2026/054453 (WO 2026/150381), PCT/IB2026/055760 (WO 2026/150383), PCT/IB2026/055870 (WO 2026/150384) and PCT/IB2026/056058 (WO 2026/154461). The profile details are as published in Internet-Draft draft-das-precision-bounded-egress-03 on 8 September 2026, and all numbers can be cross-checked on WIPO PATENTSCOPE.

Licensed under CC BY-NC 4.0. The author is open to acquihire or strategic collaboration.

Keywords

location privacy; precise geolocation; approximate location; data minimisation; privacy by default; location data brokers; SDK data leakage; advertising SDK; mobile privacy; iOS precise location; Android approximate location; W3C Geolocation API; AI agent data exfiltration; egress control; data loss prevention; geohash; geo-fuzzing; differential privacy; mobility trace; re-identification; cumulative disclosure; execution finality; non-bearer authority; Finality Sink; fail-closed; GDPR; ePrivacy; precise geolocation sensitive data; machine enforcement.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License

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