iOS Privacy Manifest: Empowering Data Privacy Evaluation

Staying informed about how apps collect and use your data is crucial in digital privacy. Addressing this, Appicaptor finds privacy-related app issues and extracts contents of the iOS Privacy Manifest. All information is correlated with results from static and dynamic analysis to provide enhanced privacy insights.

In today’s interconnected digital landscape concerns related to data privacy are in focus. App developers, platform providers and smartphone OS manufacturers face pressure to prioritize user privacy and transparency. In response to these concerns Apple has introduced an initiative: the iOS Privacy Manifest. This feature serves as a documentation how apps access, utilize and transmit privacy-related data. Appicaptor uses the information from the iOS Privacy Manifest, enabling users and companies to make well-informed decisions.

Understanding the iOS Privacy Manifest

iOS Privacy Manifests are attached to the app’s binary as property list (plist) and hold information regarding the collection and usage of privacy-related data by the app and included third-party SDKs. Not providing valid information within the iOS Privacy Manifest may lead to notifications or rejection throughout the app submission process. Strict enforcement in the App Store is scheduled starting in May 2024.

Privacy Manifest Components

When it comes to populating a iOS Privacy Manifest, there are several key data structures that developers must adhere to:

  • Required Reason APIs:  Apple introduced a class of APIs referred to as Required Reason APIs to address concerns regarding fingerprinting. Any app or SDK utilizing an API from this list must explicitly state a valid purpose for their usage.
  • Data Usage Categories: Developers must provide a breakdown of data usage categories in their privacy manifests. This includes specifying the data collected, whether it’s linked to end-users’ identities, its usage for tracking purposes, and a list of reasons justifying. Apple provides a predefined set of purposes that developers have to reference.
  • External Domain Usage: All external tracking domains used in the app or third-party SDKs must be listed in the iOS Privacy Manifest. This provides users with clear visibility into the presence of all domains utilized for tracking purposes. When tracking permission (via the App Tracking Transparency framework) is not granted by the user, network requests to these domains will be blocked by iOS.

Appicaptor: iOS Privacy Manifest as additional App Evaluation Source

Appicaptor extracts the contents of the iOS Privacy Manifest and presents them in human-readable format. But beyond that, Appicaptor correlates the manifest’s content with privacy-related findings discovered from the app binary through static analysis. Therefore, Appicaptor offers users a comprehensive understanding of how their privacy-related data is handled by the apps:

  • Privacy Transparency: By presenting the iOS Privacy Manifest contents in a human-readable format, Appicaptor users gain transparency into the app’s data collection practices.
  • Informed Decision-Making Elevated: Armed with in-depth understanding of an app’s privacy practices, Appicaptor users can make informed decisions about whether to engage with the app. Correlation of the manifest contents with static analysis findings in coordination with Appicaptor’s ability to define custom rulesets empowers companies to assess the privacy risks associated with using the app to make choices aligned with their privacy preferences and concerns.
  • Enhanced Privacy Insights: Appicaptor’s static analysis enables an examination of the app’s codebase, revealing privacy insights in addition to what is disclosed in the iOS Privacy Manifest. By correlating these findings, users gain a more granular understanding of how their data is collected, used, and shared by the app.
  • Accountability and Trust Building: Appicaptor’s evaluation reports promotes accountability and trust by ensuring alignment between stated privacy practices and actual implementation. By correlating the results, Appicaptor users can identify and address any discrepancies between the documentation and implementation of privacy-related data usage.
Uncorrelated iOS Privacy Manifest information prestenation in Appicaptor


In an era where trust between users and tech companies is important, transparency regarding data collection practices is non-negotiable. Using the iOS Privacy Manifest as an additional source of evaluation, Appicaptor enhances its app evaluation capabilities and gives users a more thorough understanding of how their data is handled by various apps. This integration enforces Appicaptor’s commitment empowering users with transparent and informed decisions regarding app security.

Appicaptor’s solution contributes to the overall integrity of the digital ecosystem by promoting transparency, accountability, and responsible data usage. By empowering users with comprehensive privacy insights and encouraging developers to uphold best practices and that the implementation is inline with documentation, Appicaptor’s approach supports a culture of trust and integrity that benefits everyone involved.

Device Fingerprinting on iOS Apps

Device fingerprinting is a technique that got popular at the end of the 90s by websites, to identify and track users. Apps have in contrast to websites often access to a much wider property range usable for fingerprinting. The unique device identification via fingerprinting across sandbox borders of multiple apps is a relevant privacy issue as it increase the possibilities for tracking users, even without requiring permissions. Many smartphone users are unaware of such practices and possibilities because such activities happen unnoticeably in the background.

Just like each person has an individual fingerprint, electronic devices are also uniquely identifiable. Through manufacturing tolerances, different hardware configurations, software properties and individual software configurability such devices become uniquely identifiable. Depending on the number and the variance of properties used, more or less unique fingerprints can be generated. Typically, several hard- and software device properties are combined through a hashing algorithm to an ideally unique bit sequence.

Motivations for app developers to include tracking libraries are: advertisement, bot detection, account takeover, spam, fraud detection and secure environment detection for payments. Mobile operating systems such as Android and iOS are aware of the user’s transparency through fingerprinting and take steps to empower the user to regain privacy whilst trying to maintain legitimate tracking objectives. The advertisement ID was introduced especially to target device tracking with a resettable ID provided by iOS. It allows advertisers to personalize ads and at the same time give the user options to reset it or hide it for individual apps. Also, Privacy Labels are added to the app store to better explain the app’s data usage to the user. Permissions and Privacy Labels should shed light on the usage of tracking, and it’s purpose during app install and usage. Apple AppStore’s licence agreement also states clear rules on data and identifier usage. However, as shown by Deng et al., many apps exist in the AppStore infringing such rules, working their way around permissions and being dishonest with their Privacy Labels. One increasing way to bypass such restrictions is to use fingerprinting techniques not requiring user consent.

Tracking Possibilities with Fingerprinting

The fingerprint generated by each app containing the same fingerprinting SDK are ideally the same on the same device. The user becomes very transparent by correlating the fingerprint with other data provided during app usage. As an example, one could think of a scenario, pictured in the following figure, where a shopping app, a search engine app and a navigation app all contain the same fingerprinting SDK. All user input of each app like recent purchases, search terms and the device location could be accumulated in the cloud under the same fingerprint. The data collection of each app alone is already a privacy risk to the user, but the combination of different app data makes users fully transparent. Such data is extremely valuable for example for advertisement companies to personally tailor advertisements for individual users.

Example of tracking possibilities with fingerprinting SDKs

Fingerprinting SDKs and Commonly used Properties

To gain insight into the current state of iOS fingerprinting and the latest techniques being used, we identified real iOS fingerprinting SDKs through systematic internet research and conducted manual static and dynamic analysis. The respective SDKs source-code was manually analysed with Ghidra and if possible, the SDK was tested in a minimal app construct. During the manual analysis we judged the SDK as fingerprinter or non-fingerprinter based on the observed behaviour and used properties. This leaves 13 SDKs that we classify as fingerprinters and further analyse in more detail. The SDKs are listed in the following table. We found that all 13 SDKs use the native iOS API to collect device characteristics. Through dynamic analysis of custom-built test apps, we have found that the collection of features shows up through temporal spikes. Most SDKs send the collected device characteristics to a server for further processing, except for OpenIDFA, DFID and DFIDSwift, which are non-commercial products. Additional passive fingerprinting may be performed on the server side. The SDKs that do not include server communication generate a hash on the device and return it. Nevertheless, we argue that fingerprinting is generally only useful when the fingerprint leaves the device. Therefore, it can be assumed that in these cases the caller of the fingerprinting framework handles the network communication. It is obvious from the following table that fingerprinting SDKs share some
collected properties, while other properties are exclusively queried by some SDKs. Very commonly used properties are: device model, identifier for vendor (similar to advertisement ID), device name, storage space properties, battery level and different locale identifiers. Other properties like device fonts, commonly used in web based fingerprinters, is rather seldom used. We suppose, due to the low entropy of this value on iOS devices. In conclusion, one can say that fingerprinters usually use multiple properties and don’t rely on a unique identifier provided through the advertisement ID in iOS.

Used device properties by different fingerprinting SDKs

Fingerprinting Access Pattern

We were able to observe access to common iOS APIs gathered in the previous step. Frida hooks were written to log access to respective APIs in a dynamic app analysis on a jailbroken iPhone X (iOS 14.4.2). In the next step, we analysed several top apps from the app store to see if fingerprinting can be observed based on API access pattern during execution.

Non-fingerprinting App

Firstly, let’s have a look at an example of an app without fingerprinting in the following figure. One can see that different properties were used during the app execution, some less, some more frequently. The accesses to the observed properties came from different caller modules (packages or libraries) from inside the app. Also, the properties were accessed throughout the whole runtime. Access to properties such as timezone and locale are typically required to properly display and run the app and totally legitimate.

Fingerprinting App

Now, let’s look at an app which we judge as fingerprinting in the following figure. One can see that from second 15 to 17, many properties are accessed in a very short timespan. Furthermore, the accesses come from the same caller module called ForterSDK, which is a known fingerprinting SDK. We observed such behaviour for all fingerprinting SDKs: Many properties are collected by the same caller module in a short time frame.

Fingerprinting Detection

As a proof of concept, we developed a fingerprinting detector to detect fingerprinting on the traces of a dynamic app analysis. The analyser is implemented as a sliding window of a certain width. The window of four seconds width slides over the event timeline and counts the number of accesses to known fingerprinting APIs per caller module. Access numbers above a certain threshold are then justified as fingerprinting. In a first test, we were able to correctly detect fingerprinting in 85% of the cases just by analysing access pattern. As further refinements, machine learning could improve accuracy as well as the inclusion of known fingerprinting SDK properties through a static analysis. Further insights on our used detection mechanism can be read in our extensive paper at ACM, published at the EICC conference 2023.


Fingerprinting as of today is used in many apps. Smartphone users are mostly unaware of the existence and the privacy issues related with such. This research lays the fundamentals to identify device fingerprinting in iOS apps. Future work will be to come up with remediation ideas to balance the privacy interests of smartphone users and fingerprinters.


This research work was supported by the National Research Center for Applied Cybersecurity ATHENE. ATHENE is funded jointly by the German Federal Ministry of Education and Research and the Hessian Ministry of Higher Education, Research and the Arts.

Note: Fingerprinting detection is currently work in progress and not (yet) part of the Appicaptor service.

iOS App Tracking Transparency – Adoption Rate of App Implementations

With Apple’s release of iOS 14.5 at the end of April, iOS app developers are required to request permission in order to track their users beyond the app’s border. While there are already complaints about the low opt-in rate published by the app analytics company Flurry, which are updated weekly, we were curious to see how the overall adoption rate for app implementations would look like.

Developers need to provide a tracking description in the apps info.plist file – together with localized versions in file InfoPlist.strings of language’s project directory – called NSUserTrackingUsageDescription that informs the user why an app is requesting permission to use data for tracking the user or the device. However, in 57.3 % of the current German Top 2000 iOS apps no tracking description was provided by the developers although the app contains tracking code. On devices with iOS 14.5 or later this causes iOS to deny the request for access to the identifier for advertisers (IDFA). So the opt-in rate of users could be higher if those 57.3% of the developers would have provided a description, so that the user is at least presented with a decision dialog.

Property %
No tracking description included but tracking code detected57.3 %
Tracking description included and tracking code detected33.5 %
No tracking description included and no tracking code detected9.0 %
Tracking description included but no tracking code detected0.2 %
Evaluation of Tracking Descriptions in German Top 2000 iOS Apps of all Categories except Games and Stickers (Appicaptor, July 2021)

Another effect we observed is a missing individualization and usefullness of the description. The table below lists the Top 10 used descriptions in the Top 2000 Apps, with 77 Apps just repeating the example text provided by Apple. Other just include placeholder text, such as “YOUR TEXT”, “NSUserTrackingUsageDescription”, “none” or even “-“.

Description #
This identifier will be used to deliver personalized ads to you.77
Your data will be used to deliver personalized ads to you.9
Dadurch können wir Ihnen relevantere Werbung anzeigen, ohne deren Anzahl zu erhöhen.7
Dies wird verwendet, um den Dienst zu identifizieren, der dich weitergeleitet hat, um die ein individuelles Erlebnis zu bieten.6
Diese Kennung wird verwendet, um Ihnen personalisierte Anzeigen zu liefern.6
Ihre Daten werden verwendet, um Ihnen personalisierte Werbung zu zeigen.6
Your data will be used to deliver personalized ads.5
Datenerhebung zur Verbesserung der App und für Werbezwecke zulassen4
Deine Aktivitäten werden verwendet, um Dein Nutzererlebnis und Werbung zu personalisieren.4
Mithilfe dieser ID können wir Dir für Dich ausgewählte Werbung anzeigen.4
Top 10 Tracking Descriptions by Occurence in German Top 2000 iOS Apps of all Categories except Games and Stickers (Appicaptor, July 2021)

This leaves us with the impression, that creating a fitting description is currently only deamed important for 1/3 of the developers. Obviously the motivation depends on the benefits the developers gain from providing a tracking description.

There are many business cases which rely on or have a benefit from cross-app user tracking. The players of these business cases (e.g., ad providers and app developers who generate ad revenue) have an interest to achieve high opt-in rates. Fitting or at least reasonable descriptions for the permission dialogue will be the key for broad acceptance rates. The current evaluation shows that (1) only a minority of apps have at least a description included and (2) that they are very unspecific.

But there are use cases which currently integrate cross-app user tracking, however it does not have a beneficial effect for the using party. For example, this is the case when an app developer integrates a runtime diagnostic library. As he is only interested in the telematic data of his app, cross-app tracking would not be his interest and for that reason he may not include the description for the permission dialogue. In this case Apple’s initiative would help to reduce user tracking from companies that provide a runtime diagnostic services with a business model of selling retrieved analytics data sets to third parties or similar use cases.

Rise and Fall of Specific Unique Identifiers

Retrieving a unique identifier may allow app developers, advertisers, analytic companies and others to identify the user’s device or the user himself. Furthermore, most of these identifiers are persistent means for tracking, advertising and marketing activities on devices. Unique identifiers might however be also necessary for certain app functionality to work as expected.

Appicaptor tracks app’s access to various unique identifiers that can be categorized in three different groups:

  • The first group refers to mobile communication relevant IDs. Examples of this category are access to the phone’s IMEIs and MAC addresses, country code of the mobile network provider, as well as the phone / voice mail number, serial number of the SIM card and mobile subscriber ID (IMSI / TIMSI) of the user.
  • The second group is identification information about the hardware or operating system given by the operating system itself. When the mobile operating system is compiled different parameters for model, hardware, serial and display size are included in the operating system build. Furthermore, a build fingerprint can distinguish different operating system builds even if the operating system version is equal.
  • The third group consists of identifiers
    • like the Android Device ID, Advertisement ID,
    • properties that the user could configure like font size / type, audio volume, timezone, display orientation lock and screen brightness, Bluetooth pairings, power saving mode configuration, audio singnal output port (speaker, headphones, Bluetooth, etc.)
    • installed app list
    • hardware parameters like cpu and set of available hardware sensors (gyroscope, barometer, …)
    • other parameters like battery or device memory (RAM and data) usage.

Every month Appicaptor evaluates the IT security quality of thousands of Android and iOS apps. The following two charts depict for each month which identifier usage is rising and which is falling. The charts plot the identifier usage (total number of apps within the 2,000 most popular apps in German Google Play Store that accesses an identifier) relatively to the identifier usage in January 2020.

Rising Unique Identifiers: identifier usage within the 2,000 most popular apps in German Google Play Store relatively to the identifier usage in January 2020
Falling Unique Identifiers: identifier usage within the 2,000 most popular apps in German Google Play Store relatively to the identifier usage in January 2020

As the relative change (given in the two charts before) does not give the perspective, which identifiers are commonly utilized and to which extent, the following table provides the absolute numbers. This table shows how many apps within the 2,000 most popular apps in German Google Play Store access an identifier in the Appicaptor analysis runs of January 2020 and February 2021. Furthermore, based on every monthly analysis run between January 2020 and February 2021 we predict a trend if the identifier usage is rising or falling based on our data.

NameIdentifier Uage
(in January 2020)
Identifier Uage
(in February 2021)
Unique Android ID1,944 1,947stable
Build model1,9471,945stable
Build fingerprint1,6791,873
Build product1,6331,760
Build brand1,5371,712
Build hardware1,1791,632
Build display1,4781,486stable
Country Code +
Mobile Network
Build serial8771,016
Mobile Country
Wifi-MAC address754717
MAC address(es)547380
Phone number281264
Subscriber ID
SIM card serial243178
Voice mail
Total number of apps that access an identifier according to Appicaptor analysis of the 2,000 most popular apps in German Google Play Store

The analysis of Appicaptor shows that the access to (generally speaking) unspecific unique identifiers (like the build related parameters) is currently rising. One might think that the access to unspecific unique identifiers (like the build brand or hardware) may be not an privacy issue since they are equal at thousands of devices/users. And that the access to a more specific unique identifier (like the SIM serial or phone number) should be more an privacy issue. However, there is more to take into consideration.

A detailed manual inspection of access patterns and looking on the landscape of the mobile value-chain shows that most of the accesses of unspecific unique identifiers are executed in 3rd party libraries, which are included in the app by the developer. Furthermore each of these unspecific information portions (if seen alone) can not be utilized to identify a specific device or person. But certain libraries access a magnitude of these unspecific unique identifiers, creating a device fingerprint from all them and transmit the data to a server backend. As an other example, an open source library of this type can be found here. It claimes to create a device identifier from all available Android platform signals, that is fully stateless and will remain the same after reinstalling or clearing application data.

The further manual inspection of other identified libraries shows as well, that libraries which probably execute device fingerprinting are utilized in many apps of different app types. A linkage between the device fingerprint and your person is possible, when you think of an app that utilizes an library that joins identifiers as device fingerprint and you give that app information about your person (name, email address, etc.). That would bring the provider of the library in the position to track your identity throughout the usage of different apps, based solely on unspecific unique identifiers.

So what can we learn from these numbers?

  • The usage of almost all specific unique identifiers are currently falling. That trend is supposed to be related to privacy preserving functions in the mobile operating systems that limit the app’s access to correct values of these identifiers. If you enable these privacy preserving functions, fake random values are provided.
  • The usage of unspecific unique identifiers is currently rising throughout all identifiers. From our perspective that rising is based on the reasoning outlined above (device fingerprinting) as well as to facilitate user identification in the presence of the current drawbacks (uncertainty if correct or fake specific unique identifiers are reported to the app by the operating system).

Therefore, in the app evaluation process one should take a look at the composition and magnitude of the list of accessed unique identifiers of an app: if many unspecified unique identifiers are accessed, this should draw one’s attention the same way as the access of an specified unique identifier should do.

Appicaptor Security Index 2018

The use of apps in enterprises requires a critical consideration of the risks. Today, we have published results of automated Appicaptor analyses for the top 2,000 free iOS and Android apps.

Chart of blacklisted apps per category, Appicaptor Security Index 2018
Blacklisted apps per category. The bars for each exemplary selected function class show the respective proportion of the three risk classes.
Appicaptor Security Index, September 2018

When assessing the fitness for corporate use, it is not very surprising that apps for processing of corporate data are quite critical. In particular, the functional class of the File Manager apps shows a significant risk of usage with 73% iOS apps classified as unsuitable for corporate use (see figure). This is even higher with Android at 86%. The reasons for the blacklisting of both platforms are a very high ratio of IT security weaknesses and privacy relevant risks.

The report also shows new test insights about security characteristics of apps using the MultipeerConnectivity API from iOS. This API allows developers to easily implement a direct exchange of data between devices via wireless communication. This can be done both authenticated and encrypted, but the appropriate options have to be used by the developer.

iOS peer-to-peer transmission with lack of encryption and authentication
Poor / Missing cryptography: Endangerment of company data during peer-to-peer transmission due to lack of encryption and authentication. Demonstrated here with AirDroid for iOS (version 1.0.3)

The Appicaptor analyses show that 40% of the iOS Apps with this functionality neither encrypt the transmission nor authenticate the communication partners. As illustrated by the example of the AirDroid iOS App (version 1.0.3), an attacker can passively read the transmissions. For 20% of the iOS Apps with this functionality the transmission is at least encrypted, but without checking the authenticity of the communication partner. An active man-in-the-middle attack would then still be possible.

Download the complete Appicaptor Security Index 2018.