What Apps Know About You: Understanding Mobile Data Collection
The apps on your smartphone know more about you than you might realize. Location history, contacts, photos, browsing habits, health data, financial transactions—apps collect vast amounts of personal information that feeds into surveillance capitalism's business model. Understanding what apps know, how they learn it, and what happens to that information helps users make informed decisions about their digital lives and supports informed policy discussions about data privacy.
Types of Data Collected
Location data reveals where you go. GPS, cell tower triangulation, and WiFi positioning all enable apps to track your location continuously or periodically. This data reveals home and work addresses, travel patterns, visits to sensitive locations like medical facilities or places of worship, and daily routines.
Contact information exposes your relationships. Apps with contact access can map your social network—who you know, how you're connected, and often how frequently you communicate. This data is valuable for marketing and for inferring information about you from your connections.
Photo and camera access enables visual surveillance. Beyond taking pictures, camera access allows apps to analyze photos in your library, extract metadata including location and time, and potentially capture images or video without explicit user action.
Usage patterns reveal behaviour and preferences. Which apps you use, when you use them, for how long, and what you do within them all generate data that reveals habits, interests, and vulnerabilities.
Device identifiers enable tracking across apps and over time. Advertising IDs, device fingerprints, and other identifiers allow data collectors to build comprehensive profiles by linking information from multiple sources.
How Data Is Collected
Permissions grant access to device capabilities. When you approve an app's request for location, contacts, camera, or other access, you authorize data collection that may exceed what you expected. Permission grants often enable more collection than apparent use cases require.
Background collection happens when apps aren't visibly active. Apps with appropriate permissions can collect data continuously, not just when you're actively using them. Background location tracking is particularly common and particularly revealing.
Third-party SDKs embedded in apps collect data for parties you may not know about. Advertising, analytics, and social media SDKs collect data that flows to those companies, not just the app developer. A single app might send data to dozens of third parties.
Inference generates new data from what's collected. Machine learning applied to collected data can infer health conditions, political views, sexual orientation, and other sensitive characteristics never explicitly shared.
What Happens to Your Data
Advertising targeting uses data to serve personalized ads. The more advertisers know about you, the more precisely they can target. This targeting is the revenue model driving most data collection.
Data brokering sells information to third parties. Data collected by apps often flows to data brokers who aggregate, analyze, and resell it. Your data may reach parties you've never heard of through these secondary markets.
Analytics and research use aggregated data for various purposes. While often claimed to be anonymized, aggregated data can frequently be re-identified or reveal information about individuals despite aggregation.
Government access occurs through various mechanisms. Law enforcement can request or compel data from companies. Intelligence agencies may access data through legal or extralegal means. Data collected commercially can become surveillance infrastructure.
Privacy Risks
Profile construction creates detailed pictures of individuals. Data from multiple sources combined with inference produces profiles that may include information you never shared and might not want known.
Discrimination can result from profiled data. When profiles inform decisions about employment, credit, insurance, or housing, data collection enables discrimination that may be invisible to those affected.
Security vulnerabilities expose collected data to breaches. Companies that collect data create targets for hackers. Breaches expose information to criminals and others who may use it harmfully.
Chilling effects occur when awareness of surveillance changes behaviour. Knowing that apps track location may deter visits to certain places. Awareness of monitoring changes how people behave even without direct harm.
Future risks are hard to anticipate. Data collected today may be used in ways impossible to predict. Changes in technology, law, or circumstance may make currently harmless data dangerous later.
Protecting Yourself
Permission management limits what apps can access. Review and restrict permissions to only what apps genuinely need. Deny location to apps that don't need it; restrict camera and microphone access; be skeptical of contact requests.
Location services deserve particular attention. Consider disabling location entirely except when actively needed. When enabled, use "only while using" rather than "always" where possible.
App auditing identifies what's on your device. Remove apps you don't use; they may still collect data in background. Review what apps you have and whether you need them.
Privacy settings within apps may offer controls. Many apps have settings that reduce data collection or sharing if you find and adjust them. These settings may not be prominent.
Alternative apps may collect less data. Privacy-focused alternatives exist for many common app categories. Choosing apps with better privacy practices reduces exposure.
Structural Limitations
Individual action cannot solve structural problems. Even careful users cannot fully protect themselves when data collection is pervasive and deceptive. Privacy requires regulatory protection, not just individual vigilance.
Privacy policies are unreadable and unread. The legal fiction that users consent by agreeing to privacy policies obscures the reality that no one reads them and they permit almost anything. Informed consent doesn't exist in current frameworks.
Market power limits choice. When dominant apps are necessary for social participation, opting out isn't truly available. Network effects lock users into platforms regardless of privacy concerns.
Regulatory Responses
Data protection laws like GDPR in Europe and emerging Canadian legislation attempt to constrain data collection and give individuals rights over their data. Enforcement determines whether these laws actually change practices.
Consent requirements aim to ensure users knowingly agree to data collection. Yet consent frameworks have largely failed to provide meaningful protection given power imbalances and practical limitations.
Purpose limitations restrict using data beyond stated purposes. Laws that prevent mission creep in data use address some concerns about unexpected uses of collected information.
Data minimization principles require collecting only necessary data. If enforced, minimization would reduce the vast over-collection that characterizes current practices.
Conclusion
Apps know far more about users than most realize—location, contacts, photos, behaviours, and inferences that extend to sensitive domains. This data collection serves business models built on surveillance, creating profiles that enable targeting, discrimination, and risks that individuals cannot fully anticipate or prevent. While personal protective measures help, structural solutions require regulatory intervention that constrains collection, protects against misuse, and gives individuals meaningful control over their digital selves. Understanding what apps know is the first step toward demanding the privacy protections that current systems fail to provide.