Articles tagged:   COVID-19

Population-wide antigen testing for COVID-19 in Slovakia

Slovensko plánuje celoplošné antigénové testovanie na COVID-19 a z pohľadu na tlačovky to vyzerá, že to robí nie práve informovane. Tento príspevok obsahuje interaktívny nástroj na odhadovanie a výpočet chybovosti týchto testov na populácii. Na výpočet toho, koľko pozitívnych prípadov test zachytí (true positive) či koľko negatívnych ľudí prehlási za pozitívnych (false positive) je treba niekoľko parametrov. Parametre Populácia, Účasť a Nakazení sú odhady, pričom odhad nakazených v populácii (a aj motivácia za týmto príspevkom) je z príspevku Richarda Kollára. Odhad Senzitivity testu je z porovnávacej štúdie FN Motol. Odhad Špecificity testu je pomerne optimistický a väčšina štúdii ho pre plánované antigénové testy určuje nižšie.

Nástroj je interaktívny a odhady parametrov je možné meniť.

Metóda 1

Populácia
Účasť
Nakazení
Senzitivita testu
Špecificita testu
Pravdivo pozitívni Falošne pozitívni Pravidivo negatívni Falošne negatívni Netestovaní pozitívni
Kód ktorý robí výpočet môžete nájsť nižšie (JavaScript) a vo forme Jupyter notebooku aj na binderi.
mybinder.org
// Calculate the population that will get tested
let tested_population = population * participation;

// Calculate the infected among the tested and non-tested
// Assumption that attendance is uniform among infected and non-infected
let tested_infected = infected * participation;
let tested_clean = tested_population - tested_infected;

// Calculate the true/false and negative/positive from the tested sample,
// with given sensitivity and specificity
let true_clean = tested_clean * specificity;
let false_infected = tested_clean * (1 - specificity);
let true_infected = tested_infected * sensitivity;
let false_clean = tested_infected * (1 - sensitivity);

// Calculate the missed infected
let missed_infected = infected * (1 - participation);
return {
    "true_negative": true_clean,
    "false_positive": false_infected,
    "true_positive": true_infected,
    "false_negative": false_clean,
    "missed_positive": missed_infected 
};

Metóda 2

Populácia
Testovaní
Senzitivita testu
Špecificita testu
Pozitívne otestovaní
Infikovaní testovaní Neinfikovaní testovaní Infikovaní celkovo Neinfikovaní celkovo
Pravdivo pozitívni Falošne pozitívni Pravidivo negatívni Falošne negatívni Netestovaní pozitívni
Kód ktorý robí výpočet môžete nájsť nižšie (JavaScript) a vo forme Jupyter notebooku aj na binderi.
mybinder.org
let attendance = tested / population;
let tested_negative = tested - tested_positive;

// Calculate the number of infected among the tested
let tested_infected = (specificity * tested_positive - (1 - specificity) * tested_negative) / (specificity + sensitivity - 1);
let tested_clean = tested - tested_infected;

// Assumption that attendance is uniform among infected and non-infected
let total_infected = (tested_infected / tested) * population;
let total_clean = (tested_clean / tested) * population;

// Calculate the missed infected
let missed_infected = total_infected - tested_infected;

// Calculate the true/false and negative/positive from the tested sample, with given sensitivity and specificity
let true_clean = tested_clean * specificity;
let false_infected = tested_clean * (1 - specificity);
let true_infected = tested_infected * sensitivity;
let false_clean = tested_infected * (1 - sensitivity);
return {
    "tested_infected": tested_infected,
    "tested_clean": tested_clean,
    "total_infected": total_infected,
    "total_clean": total_clean,
    "true_negative": true_clean,
    "false_positive": false_infected,
    "true_positive": true_infected,
    "false_negative": false_clean,
    "missed_positive": missed_infected 
};

Vysvetlivky

  • Pravdivo pozitívny: Prípad kedy bol pozitívny človek správne identifikovaný testom ako pozitívny. Z populácie sa tak izolujú symptomatickí aj asymptomatickí ľudia a preruší sa tak táto vetva prenosu ochorenia.
  • Falošne pozitívny: Prípad kedy človek nemá COVID-19 avšak bol testom falošne identifikovaný ako pozitívny (bude absolvovať karanténu a následne si môže myslieť, že COVID-19 už prekonal a má imunitu).
  • Pravdivo negatívny: Prípad kedy bol negatívny človek správne identifikovaný testom ako negatívny.
  • Falošne negatívny: Prípad kedy človek má COVID-19 avšak bol testom falošne identifikovaný ako negatívny (a bude mať rozšírené možnosti pohybu na verejnosti).
  • Netestovaný pozitívny: Prípad kedy človek má COVID-19, avšak nezúčastnil sa celoplošného testovania (a bude mať obmedzené možnosti pohybu na verejnosti).

Analysis of the Covid19 ZostanZdravy app - Contact-tracing

Covid19 ZostanZdravy

This post analyzes the Slovak contact-tracing app Covid19 ZostanZdravy from a security and privacy perspective. The app is being developed by volunteers from Sygic, but is officially running under control of NCZI, the National Health Information Center, with data ownership by UVZ, the Public Health Authority of Slovakia (see the privacy policy). This analysis was performed from publicly available sources, which was possible as both the app and backend are open-source (the analyzed commits were 400aa52, 2710f09 and f9b9d2c). The text below represents the issues I see in the current workings of the contact-tracing part of the app and provides an outlook on fixing them and moving forward. The analysis represents a best effort analysis done in a day, it might contain errors, or I might have misrepresented something, I am open to comments .

Privacy

The app does not use an established contact-tracing protocol, such as DP-3T, PEPP-PT NTK or ROBERT, but instead uses a custom designed protocol to perform contact-tracing. This is because the app predates those protocols by a few weeks. The contact-tracing protocol is a BLE-based contact-tracing protocol with static IDs that roughly works as follows:
  1. The user installs the app, which generates a deviceID a random UUID of the device, enrolls this device with the server and receives back a profileID which is an unsigned integer, assigned in a increasing sequence by the server.
  2. The app then broadcasts the profileID of the device on BLE and listens to other broadcasted profileIDs of other devices.
  3. The app then periodically upload a list of seen profileIDs to the server. This upload and all of the app's interaction with the server is authenticated by the deviceID which is sent to the server in every request and is kept on the device otherwise. The uploaded list of contacts used to contain the time and duration of the contacts, but this was abandoned and instead only the day of contact is uploaded.
  4. When the user becomes infected, the actions of the protocol become unclear, as the open-source backend is just an HTTP API, the administration of the whole system is done through an admin app that interacts with the backend, but is not open-source. However, something can be deduced from the API offered by the backend, as it offers one administrative call to query the seen profileIDs by a given device (identified by both the deviceID and profileID). This call is likely used by the admin app to query the contacts of a newly infected user and send alerts/quarantine recommendations to them. It is important to note that this call reports one-sided contacts as reported by the users.

This approach clearly provides the whole contact graph of a user's device to the server, whether the user is infected or not. Such a contact graph, while it is pseudonymous, leaks significant private information about the users to the server (see this document, section 4).

Contact reporting

As described above, it is likely that the reporting of contacts of an infected user uses only one-sided contacts submitted by the user's device, i.e. when querying the contacts of a user X, the contacts of all users are queried for X's profileID (see the code). Which might make sense, if one accounts for the possibility of some devices going offline and not uploading their contacts. If contact reports from both parties were necessary to report a contact, this might pose problems. However, this implicit trust of user's reported contacts, together with the way profileIDs are assigned (unsigned increasing integer sequence, see the code here and here) creates an attack on the system, in which an attacker can get the infection status of all of the users.

  1. Attacker first creates a new profile, and receives back their profileID. As profileIDs are generated incrementally, the attacker can now enumerate all previously registered profileIDs.
  2. The attacker creates a new profile for each of the existing user's profiles.
  3. Then the attacker will report a contact from each of his profiles with exactly one of the legitimate user's profiles, i.e. attacker's profile #1 reports contact with user profile #1, attacker's profile #2 reports contact with user profile #2, and so on.
  4. When any of the users registered before attacker's profile registration are confirmed infected, the query for their contacts will always include the one attacker's profile and the attacker will get a notification of being in contact with an infected user.
  5. There is also the possibility of extending this attack to complete deanonymization of an infected user, by placing BLE listening devices in particular public places, together with a camera capturing the area, and then correlating the captured broadcasts with the camera view of the area (see here). This data collection can be performed even before the attack itself or before the user's infection.

If however, the implicit trust was one-sided the other way, i.e. querying the contacts of a user X would trust their reported contacts a different attack would be possible, one that would mark all users as having contact with an infected person.

The attack would work as follows:
  1. Attacker registers a profile with the server, and receives back their profileID. As profileIDs are generated incrementally, the attacker can now enumerate all previously registered profileIDs. They can not however spoof messages to the API as users with those profileIDs as deviceIDs are required for that, and those are random UUIDs that contain enough entropy.
  2. The attacker can however report any and all profileIDs in use to the server as contacts, possibly daily for some period of time.
  3. The attacker can now give the account details/device with the account details to a likely infected cooperating person, which will get tested and obtain a confirmation of infection from a health authority. The person then confirms their infection with the attackers account details, which immediately marks all of the users in the system as exposed to an infected person.

Modifying the system to rely on both sides of an encounter to report it might seem like an easy fix, however that brings the aforementioned issues of false-negatives created by devices going offline, or devices with different bluetooth strength (where only one device saw enough broadcasts of the other device to report a contact) and so on. The current system with predictable and static user IDs will likely always suffer from similar attacks.

Using a custom contact-tracing protocol, as the system does, is a security risk even if the above attack is fixed, as proper specification and security analysis is necessary to get it right. One can get both of those by using an established protocol such as DP-3T. As the cryptography community mantra rightfully states, Don't roll your own crypto!

Build reproducibility and deployment

The three components of the system, the Android app, the iOS app and the backend server are all open-source, which is quite nice from an analysis perspective and also the bare-minimum a contact-tracing system should be.

There is however no transparency over the build and deployment process, e.g. what versions of code actually run on the server, or are provided in the respective app stores. The Android app does not contain the full configuration and it is thus not possible to build it reproducibly such that the built APK matches the app store APK perfectly.

Having build reproducibility for a privacy sensitive app is important, to ensure that code can be analyzed and that arguments from this code analysis can be applied to the deployed app. Also to make decompilation and analysis of deployed apps not necessary apart from a comparison of the app's hash.

Specification and documentation

The system lacks any proper specification, of the contact-tracing protocol, backend API or really any component. Without a detailed specification of all of the system's components and their responsibilities and behavior, proper analysis is resource-intensive if not impossible. This can be seen from my statements about the attacks above, where an unavailable component of the system, the admin app, makes decisions that influence how and if an attack would work. Without this specification, which should have been created before implementation took place, more vulnerabilities in the system cannot be ruled out, they will however remain harder to find and fix.

The components also lack documentation, apart from a README here and there. Having properly documented components would make security analysis of the system easier, as well as help new contributors to contribute to the project.

Tests

The android app contains no tests at all, the iOS app contains a test directory that contains no tests. The server is the only component with any tests, and contains a few tests for the push-notification service, SMS messaging service and a few unit tests for the core repository. This absence of tests is a serious issue for a privacy sensitive app, as the likelihood of errors in the code with absolutely no tests is high.

Calibration and real-world testing

The contact-tracing capabilities of the app have not been properly tested in the real-world, to the best of my knowledge. Such testing is necessary for proper calibration of what an epidemiologically significant encounter is and how it manifests in the BLE broadcasts. Modern devices have strong capabilities to both broadcast and receive the broadcasts, if any sequence of correctly received broadcasts longer than 5 minutes is counted as an encounter (as currently done in the app), the number of false-positives would likely be quite high.

Calibration and real-world testing is currently being performed by the DP-3T team, using an app built using their decentralized contact-tracing protocol, even before the deployment of the app in Switzerland (see here and here).

Other solutions

In comparison with current contact-tracing efforts and plans of different countries, the app is clearly the least privacy-preserving, due to using the aforementioned privacy issues (full contact graph on server, attacks possible, static and predictable IDs used).

The DP-3T project presents a decentralized privacy-preserving approach to contact-tracing, with strong guarantees, a detailed specification, published SDKs and extensive security analysis. It is also backed by a large group of researchers from the security & privacy area. This approach will be deployed in Switzerland (app). There has also been extensive work on interoperability of contact-tracing protocols, focusing on DP-3T (here).

The situation in the UK seems worse than the case of Switzerland, the NHSX/NCSC recently released a specification for a custom centralized contact-tracing system, which does not have privacy-preserving properties (see here for an analysis by Martin Albrecht and here for an analysis by Kenny Paterson).

There have been several statements from hundreds of scientists and researchers mainly in the fields of security & privacy that called for a responsible, privacy-preserving by design, approach to contact-tracing. See here and here. These statements endorse the decentralized privacy-preserving approach taken by DP-3T and clearly advise against the centralized approach taken by the Covid19 ZostanZdravy app (obviously without directly mentioning it).

Conclusions and recommendations

I believe the app, as it is now, presents a significant risk from a privacy perspective. The following list summarizes the issues presented:

  • The app reveals the full contact graph of all of its users to the server.
  • The app uses static and predictable user IDs.
  • The app allows for an attack in which an attacker gains the infection status of all users.
  • The app is not build reproducibly and thus correspondence between the deployed apps and the sources can not be easily confirmed.
  • The app has no specification and documentation.
  • The app has almost no tests.
  • There was no public security analysis of the contact-tracing protocol or the apps.
  • There was no calibration and real-world testing of the app and system.

When comparing the app to the principles outlined in the Joint Statement on Contact Tracing, the app fails all but one.

  • "Contact tracing Apps must only be used to support public health measures for the containment of COVID-19. The system must not be capable of collecting, processing, or transmitting any more data than what is necessary to achieve this purpose." The app collects the full contact graph of all users, which is unnecessary.
  • "Any considered solution must be fully transparent. The protocols and their implementations, including any sub-components provided by companies, must be available for public analysis. The processed data and if, how, where, and for how long they are stored must be documented unambiguously. Such data collected should be minimal for the given purpose." The data collected by the app is not minimal.
  • "When multiple possible options to implement a certain component or functionality of the app exist, then the most privacy-preserving option must be chosen. Deviations from this principle are only permissible if this is necessary to achieve the purpose of the app more effectively, and must be clearly justified with sunset provisions." The contact-tracing protocol implemented is clearly not the most privacy-preserving, but likely the simplest.
  • "The use of contact tracing Apps and the systems that support them must be voluntary, used with the explicit consent of the user and the systems must be designed to be able to be switched off, and all data deleted, when the current crisis is over." The app is currently voluntary.

I want to stress that an analysis like this one should have been performed long before the app achieved current levels of deployment. A way to fix some of the issues above would be to move the app to the DP-3T contact-tracing protocol, which has SDKs available for both Android and iOS, and has passed significant security and privacy analysis. This would fix the privacy and security issues inherent in the protocol used, but also help with other issues, as the need for a full specification would be lower, the code to document would be simpler and there would be less code to test. Calibration and testing issues would be also resolved by the currently ongoing testing by the DP-3T team.

One practical issue that I did not mention, as it does not pertain to security or privacy, is that of Bluetooth broadcast issues on iOS. This would be resolved by using DP-3T as well, since the iOS SDK of DP-3T plans to utilize the Apple provided contact-tracing APIs, when they become available.