The Dark Side of SwissCovid

"Ich chume nöd drus mit dem Zügs."
("Je ne comprends pas ce truc.")
(Ueli Maurer - member of the Swiss Federal Council - 4.7.2020)
(impossible to translate)

We followed the evolution of SwissCovid and did our own analysis on it. Lots of consensual opinions have already been expressed. We wish here to present an alternative viewpoint about SwissCovid. Our position on SwissCovid is that

The privacy-by-design and transparency claims are smoke screens. Although it is important that we all protect ourselves and others with such tools, we should not deceive ourselves with unsatisfied promises.


What's new? A comment statistics not done during weekends was added in the usefulness section. A reference to the coronadetective demonstrator was added in the attacks section. A paper about ethics was added in the conclusion.

Disclaimer: We are strong advocate of transparency and objectivity in scientific communication. We reckon that the pandemic has made tools such as proximity tracing (sadly) unavoidable. We respect the political decision to deploy it. The political decision took the way of a voluntary acceptance, which requires trust to be effective. We do not believe in a conspiracy. We have no doubt all involved people, organizations, and companies are in good wills and did their restless best to fight the pandemic. At the same time, we know that the road to hell is paved with good intentions. It has been claimed many times that data is anonymous, that there is no threat, that the app is transparent because of being open source by law, that people should not be scared, that fear is irrational, that communication is monopolized by conspiracy theories. By trying to communicate over-simplified information and giving paternalistic advises for adopting SwissCovid, SwissCovid has failed to be transparent. By failing in providing fair information, by adopting an opaque proprietary solution, and by bypassing the law, we feel that trust is broken. We believe that showing all these facts is helping to support transparency. We do not think open information would affect the acceptance by people because contact tracing is a necessity. People who support SwissCovid will continue to support and skeptical people will be confirmed in their skepticism. We think people deserve to know facts.

Disclaimer 2: The content of this page does not reflect the position of EPFL. EPFL, by the voice of its president, states that SwissCovid is "super-smart", reaches "the highest possible standard on what we can do now today", "influences our Californian friends", but that "Swiss people have difficulties to be proud of what they do" (translated).


SwissCovid is based on the DP3T project. DP3T was announced on April 1, 2020 (as part of the bigger project PEPP-PT). Our early analysis of DP3T was published on April 8: It is worth saying that this report was originally meant to be read by the designers of DP3T only, but that they suggested to make it public. This is why it is available there.

In early April, a fight occurred between developers of centralized and decentralized systems, within the PEPP-PT project, with the conquest of countries for the adoption of one or the other system in background. DP3T, which was developing a decentralized system, split from PEPP-PT and won the battle. We reckon that

However, arguments against centralized systems have been overly exaggerated and the ones in favor of decentralized systems have been oversold to a level that we found unethical (at least, against the objectivity rules of scientific communication). We do not think that one approach protects privacy better than the other. Furthermore, other ways have not been investigated enough.

Since all these was also clear for policy makers, we can wonder why decentralized systems succeeded to conquer most of European countries. This is most likely due to the heavy support of Apple and Google. It is known (with the previous experience of Singapore) that access to Bluetooth can drain the battery of the phone quite a lot. Apple and Google announced that they would provide an optimized access to Bluetooth to decentralized contact tracing apps only but would keep on restricting it to other applications.

Our study about centralized versus decentralized systems, as well as directions for a 3rd way, was published on May 6, 2020:

In parallel, we worked with colleagues from France to explain in an as non-technical manner as possible, the risks which eventually come with any automated contact tracing. Our article is available in French (English version available from the link) on April 21, 2020:

Own Analysis

Before the deployment of SwissCovid, there has been a test phase for a pilot version. As tester of the pilot version, we were required to report on our findings. We posted our report on June 5, 2020. However, the report was held under a responsible disclosure clause until June 16, during which the law on SwissCovid was discussed and approved. Meanwhile, our report has been heavily criticized, although we could not even publish it. The link below tells the story of our report and describes the compliance issue. It also gives links to the documents. In summary, our report shows that Our compliance analysis concludes that

Apple & Google

Our main concern about SwissCovid is that the app is outsourcing nearly all its tasks to the Apple-Google implementation called GAEN (as for Google Apple Exposure Notification) and that GAEN is out of public or national control: there is no source code nor any way to verify how it works. As discussed in the above document, this is not compliant with the spirit of the law. We tried to reconstruct the chronology which ended up in this state of affair: Meanwhile, there are bugs in GAEN. They are not easy to find because of no source code. For instance, rolling the ephemeral identifiers and the visible random addresses they look to come from is supposed to be done synchronously. However, we noticed it is not: there is always one broadcast with the new address and the old identifier. This leads to the easy Little Thumb attack to link identifiers. We also noticed that, in some phones, the Bluetooth stack crashes silently and SwissCovid is sending identifiers but collecting none. The user would wrongly believe to be protected by the app.

What Google and Apple do with the data collected by Bluetooth is also out of control. Finally, the resulting difference between centralized and decentralized systems is that, on the former case, data is stored in a central server which is maintained by local authorities, while in the latter case, data is stored in a distributed server (the GAEN memory of the phones) which is maintained by Apple and Google. In the former case, trust is based on a democratic system. In the latter case, trust is based on a commercial system.

France had a controvery about StopCovid - the French app which relies on a centralized system - storing more information than what is necessary. Contact tracing indeed aims at storing only contacts which last long enough and at a close distance (in Switzerland: less than 1.5m during 15 minutes). It was shown that StopCovid was actually sending all contacts (either brief or distant ones) to the central server. A similar controversy could occur in Switzerland: GAEN actually stores all contacts. They are stored locally on the phone, in this distributed decentralized server. However, since there is no control about what Apple and Google are doing with those stored contacts, the same controversy is possible. Apparently, this problem was fixed in France. It is unlikely it will be in Switzerland as it depends on GAEN.

Apple and Google could further abuse their dominant position to have their parallel contact tracing system, in order to identify people who had any contact (long or not, at distance or not) with a targetted category of people. This category would not necessarily be the category of diagnosed people. For instance, it could be the category of people who used a given commercial product. Identifying the contacts of users of a product could be a good target for advertisement. If maliciously used, GAEN could become a gold mine for Apple and Google.

ENE actually strengthens the dominant position of Apple and Google. Citizens have no longer anything to say about it except taking it as a whole or refusing it. This is a major loss of government's digital sovereignty. So far, there is no plan in switching from SwissCovid to ENE, but we cannot predict what Apple and Google will impose next.

How it Works

The app and GAEN. SwissCovid devices permanently broadcast an ephemeral identifier which changes every 10-12 minutes. They are called RPI as for Rolling Proximity Identifier. Every ephemeral identifier is derived from a daily key which is selected at random. This daily key is called TEK as for Temporary Exposure Key. Given the ephemeral identifier, we cannot recover the daily key. However, given the TEK, we can recover all RPI of the day. As specified in the Apple/Google specifications, GAEN is We will see below that GAEN plays two more roles.

If a user is diagnosed, his app gets the daily keys of the last few days from GAEN and uploads them on a public server. Those uploaded TEKs are called the Diagnosis Keys.

This server can be observed by anyone. Apps regularly download the diagnosis keys and give them to GAEN. GAEN checks if those keys derive one of the collected ephemeral identifiers RPI, to check if a diagnosed user was in contact. GAEN reports the contacts with diagnosed users with information about the date, the duration, and the distance to the app. The app decides to raise a notification or not.

SwissCovid Principle
SwissCovid Principle:
    (1) phones (GAEN) generate daily keys, derive and exchange ephemeral identifiers; (2) Alice gets diagnosed; (3) Alice's app gets the keys making identifiers she used from GAEN and reports to the server; (4) Bob's app downloads the reported keys, GAEN compares with the collected identifiers, and reports matchings to the app; (5) Bob's app decides to raise a notification.

To estimate the distance of a contact, phones compare the power of the Bluetooth signal when being sent and when being received. However, the power of the sending signal is encrypted (using the daily key) inside the Bluetooth message. Consequently, it is not visible by the receiving phone until it obtains the daily key of the sender (because he was diagnosed and reported). Hence, it is only at the time of the comparison that the distance can be estimated.

Similarly, the ephemeral identifiers are rolling every 10-12 minutes. Normally, a receiver cannot link two identifiers until it receives the daily key which derived them. Hence, the duration of a contact (and that it was at least 15-minute long) can only be determined at the time of the comparison.

The server. The "server" is actually an infrastructure of several servers within a network. The app regularly downloads reports from the server and also the configuration parameters to be used to calculate the at-risk notification. Both types of downloads are supposed to be done by millions of devices every day. To provide the download service reliably, SwissCovid uses a Content Delivery Network (CDN) which is provided by Amazon. When we try to download from anywhere in the world, it is a local Amazon server which responds. The content is obtained from the servers of Swiss Federal offices and signed by them so that Amazon cannot tamper with the content.

The "server" also includes another site where to upload daily keys after diagnosis. As this operation is (hopefully) rare, there is no need for a third party service for this operation. Essentially, a diagnosed user is in contact with the public health authorities which give them an access code, called covidcode, of 12 decimal digits. With this 12 decimal digit, the app gets from another server another code (namely, a JWT code) which allows the app to upload the daily keys on the server.

One problem is that the network sees all requests to the server made by the app and can possibly identify the phone. One trick to protect privacy is to make "fake" queries once a while at random. Queries are encrypted, so the network cannot see if they are fake or real.

What the app is doing. We can list the roles of the app below.

Except for the decision to notify, the app is not processing any data. It is only passing information between the GAEN, the servers, and the user.


Regarding the ability of SwissCovid to capture close-enough and long-enough encounters, we can just observe that it is a controversial topic. (We can see discussions here and there.) Lots of tests were made during the design of SwissCovid. We have not seen any result about it. However, some independent project observed on June 26 that capturing those encounters does not work in a tram. Like other results in this field, this result has not been confirmed or denied by peers. However, we can see that the radius for contact detection in SwissCovid was increased on July 6, as explained in the following article. It was increased again on September 11 as explained by FOPH. Increasing this distance has no impact on the previous computation which assumed that encounters are always captured. However, it may increase the number of false at-risk notifications which we do not know, and henceforth the number of quarantines. A document from FOPH explains the impact on those parameters in exposure notification. There, we can read that in a lab experiment, the probability that SwissCovid thinks of a close proximity (attenuation less than 55dB) when the distance is 1.5m is 57.3%. When the distance is 3m, this probability becomes 45.6%. So, the difference it makes between 1.5m and 3m is tiny.


To know if SwissCovid is useful at all, we should wonder how many people satisfy all following criteria: In any other case, either SwissCovid does not notify, or the notification is not taken into account, or it was a false alarm, or it was not telling any new information. Those cases can only be measured by appropriate surveys from the health authorities. It is not available at the moment. Hence, we shall look at other metrics.

The usefulness of SwissCovid can be analyzed with several factors. First of all, FOPH reports to FSO an estimate for the number of "SwissCovid activations" per day as well as the number of entered covidcodes and a number of downloads. FOPH also reports the number of COVID-positive cases in Switzerland.

Second, we can compare the number of reported keys to the server and the official number of diagnosed cases in Switzerland. The site below is comparing the data we obtain from several countries which use GAEN-based contact tracing. We should keep in mind that a diagnosed user typically reports keys for the previous few days. We should also be careful that FOPH was inserting 10 fake keys every day to "exercise" the software until July 17. We could see that since end of September, Switzerland is the only country (together with Spain and Guam) not to report statistics during weekends.

The estimate for the "number of activations" was changed on July 23. Before July 23, it was estimated based on the number of requests to the server to retrieve the configuration parameters. In the source code, we can see that the app is configured to make such query every 6 hours. FOPH has no means to figure out if two different queries come from the same app (otherwise, it would be a violation of the design principles). Actually, the queries are not directly visible by FOPH as FOPH is outsourcing the reception and treatment of those queries to Amazon. Hence, the "number of daily activations" was simply the total number of queries (provided by Amazon) divided by 4 (because always running apps makes queries 4 times per day). However, this rather indicates an average number of queries. For instance, 1 million of activations could be an average number between 500'000 nightly activations and 1.5 million daily activations. Someone who was activating his app for only one minute every 6 hours was counted as a full activation although someone who was activating his app only when useful (e.g. when taking public transport or at the restaurant) was counted as a fraction of activation. However, the second user was clearly making an effective use of the app compared to the first who was not using it at all. Hence, the figures provided by FOPH was probably underestimating the effective number of active users.

Since July 23, the "number of activations" is estimated based on the number of fake reports that the app submits at random (to hide from the network when it is really submitting a report). In the source code, we can see that the next fake report is scheduled after a random duration which follows an exponential probability distribution with an average of 5 days. Hence, FOPH essentially multiplies the daily number of received fake reports by 5. This is a probabilistic estimate which has a standard deviation in the 2000-3000 range (the square root of 5 times the number of apps which is about one million). Hence, this number is rounded with a 10'000 precision to be significant. With this method, an app which is activated a few seconds every day is fully counted (as its scheduled fake report may be sent during these few seconds and another fake report can be scheduled). Hence, the "number of activations" estimates the number of apps which have been activated at least once during the day. It may over-estimate the number of apps which are always running whenever running it is useful. It may also have a few days delay to reflect the reality: if the number of apps changes a lot today, it may take a few days to observe it on the estimate.

We however stress that this number of activations is based on the received queries but nothing proves that they were made by a SwissCovid app. Indeed, anyone could buy several millions of malicious queries from a botnet on the darknet to maliciously inflate this number. (This would be illegal.)

Another interesting measure of usefulness is based on the number of reports from diagnosed users. This corresponds to the number of entered covidcodes. If, during a given period, we have a total of r reports of diagnosed users for c positive diagnoses, we can deduce the probability rc for a random diagnosed person to be a willing-to-report SwissCovid user. We represent below the obtained ratio by using the official figures.

ratio entered covidcodes over number of diagnosed cases
The goal of SwissCovid is to notify a (possibly contaminated) person who encountered close enough and long enough (in an epidemiologically relevant sense) a (possibly contagious) person who was diagnosed. When this event happens, the probability that SwissCovid succeeds to notify the possibly contaminated person is the probability that all what follows occurred:
  1. the contaminating person uses SwissCovid and is willing to report using SwissCovid after diagnosis;
  2. the contaminated person is an active SwissCovid user;
  3. both had their SwissCovid active when they encountered;
  4. SwissCovid succeeded to notice their encounter.
We take the most favorable case assuming that the last two conditions always occur. (The last one will be discussed hereafter.) We assume that both participants are uniformly distributed and independent. The first condition occurs with the probability rc from the plot, which is between 9% and 18%. The second condition occurs with probability up, where u is the number of active SwissCovid users and p is the total population. We can approximate it to 21% based on the recent figures from FOPH. (u=1'800'000 and p=8'600'000.) Hence, the probability that SwissCovid spots this infecting contact can be approximated to This approximation assumes ideal probability distributions. We obtain e=3% with most favorable figures. This is probably the order of magnitude of the probability for SwissCovid to spot an infecting contact. The computation is however very imprecise due to the assumptions.

The next interesting question is whether SwissCovid, which spots an infecting contact, is able to alert infected people before a human-based contact tracing does. Indeed, in between the time the infecting person is diagnosed, the time he reports (he has 24h for that), and the time the infected person's app downloads the new report (which can take a few more hours), the traditional contact tracing may be done. Most likely, SwissCovid is only useful when the contaminated person is completely unknown by the contaminating person, e.g. in the case they encountered in public transportation or they were seated close to each other in a restaurant.

FOPH also communicates the number of entered covidcodes. However, the SwissCovid design makes it impossible for FOPH to determine how many at-risk notifications were raised and how many of them led to a diagnosis case. The protocol will not reveal this. However, users receiving a notification may call a infoline, as recommended by the app. Statistics through the infoline is feasible.

The August 28 press conference. Interesting figures were announced by FOPH: since July 20, 13 people did a test after being notified by SwissCovid and this test revealed positive. We do not know if this was helpful to prevent them from contaminating other people. However, we can see that 13 positive cases out of 7483 (in this period) are spotted by SwissCovid. This is 0.17% of all positive cases. Those 13 cases are also to be compared to 857 entered covidcodes in this period. Hence, 1.5% of entered covidcodes generate a useful notification. It is argued by SwissCovid promoters that those numbers can only increase with the adoption of SwissCovid. It is actually weird that this number 13 is the only figure from FOPH which is not given over the period of the last 7 days. The same press conference also reveals that "over the last 7 days", 250 covidcodes were entered (for 350 generated covidcodes, for actually 800 SwissCovid users who were tested positive). By entering those codes, among the notifications to other users they generated, 150 contacted the infoline. How to compare the above 13 cases since July 20 and the 150 infoline contacts over the last 7 days? If we estimate that about 700 people contacted the infoline for the same reason since July 20 and that they include the 13 cases, this means that SwissCovid generates a population of people calling the infoline less than 2% of among whom are useful to identify. Contrarily to the above computation, there is no reason to believe that this fraction can increase with adoption. In comparison, the fraction of positive tests among all the tests being done in Switzerland was around 3.2%. It is now around 15-20% at this time while the 2% from SwissCovid did not increase. Hence, SwissCovid is clearly underperforming.

Early Evidence of Effectiveness of Digital Contact Tracing for SARS-CoV-2 in Switzerland. A paper posted on September 4 gives other information. This paper is cowritten by 20 scientists which are mostly from EPFL, but also from FOPH and several Swiss universities. Besides, eight of the coauthors are members of the Swiss National COVID-19 Science Task Force. Hence, this paper is likely to be the mainstream supporting argument of SwissCovid. The original paper was revised on September 19 by giving new figures. Below, we give the table of both figures (and the number H from the August 28 press conference) but we comment only on the latest ones.
28.8.2020 13
4.9.2020 8522 1593 1054 874 5491 4426 331 26 37 7.8-28.8 96 53 48 82 60 23
19.9.2020 12'456 2447 1645 1695 7842 6380 487 41 65 7.8-11.9 235 148 127 185 132 46
The figures given by this paper are as follows.

The paper derives a smart formula epsilon=n/( to measure the effectiveness of SwissCovid, where n=65 (I) (the adjusted number of people who get positively tested because of SwissCovid), c=1645 (C) (the number of entered covidcodes), and mu=16.7% (the proportion of active users in Switzerland). They obtain epsilon=0.24 which is comparable to the similar value obtained in known studies about the efficiency of contact tracing (which obtained 0.23 and 0.38). The conclusion of this paper is that SwissCovid is "an effective complementary tool for controlling the spread of SARS-CoV-2". It is a bit strange that such epsilon suffices to reach this conclusion as we could easily make an app reaching a much higher epsilon: just make an app doing nothing but alerting all users. We would obtain epsilon=8000... We could also alert all users at random with a fixed probability to obtain the epsilon that we want. What this analysis is missing is the ability to spot positive cases without making too many false alerts. All evaluations on medical tests report such analysis but SwissCovid. Finally, this paper lacks of verifiable results and critical self-analysis on the relevance of the results. The adjective "early" in the title "Early evidence" rather means "pseudo-scientific". We wonder whether such a biased paper, cowritten by 20 renowned experts, serves science well.

The contribution of the SwissCovid digital proximity tracing app to pandemic mitigation in the Canton of Zurich. On October 29, a new paper was posted. It is mentioned as an "in progress" document (v1.5 on October 29) in which a single author is mentioned as a "corresponding author". This author is a co-author of the previous paper and also member of the national COVID-19 science task force. Although this new report provides more precise figures and computations, by focusing on the Canton of Zurich, it was again written in a biased manner, with a clear objective to reach a pre-written conclusion: that SwissCovid is useful and that more people should use it. As usual, the release of this report was synchronized with the release of articles in the press (like in Le Temps).

The report ignores the comments which were mentioned above and uses the number of tests which were done with SwissCovid as a reason in the same way. It provides new figures for the Canton of Zurich during September 2020 (J): 1715 (A) cases, 429 (B) of which being SwissCovid users, 344 (C) entered covidcodes, 756 (D) calls to infoline, and 30 (H) cases with a SwissCovid alert as a reason to be tested. Contrarily to the previous paper in which numbers are real counts, most of these numbers are estimates following one or two methods. The value H is obtained in the same way as in the previous paper, as provided by FOPH, and confirmed by multiplying a total of 1715 cases by a percentage 1.9% coming from a "Zurich Sars-CoV-2 Cohort Study". It is not clear at all where this 1.9% is coming from, but given that it may comes from the health authority of the Canton of Zurich, who gave the data to FOPH, the source of the data is likely to be the same in both computations. The canton has a population of 1.54 Million (T).
29.10.2020 1715 429 344 756 30 1-30.9 (ZH) 1418 166 3000 1'540'000

The new report makes interesting estimates for new numbers.

Since there were overall 3000 (S) quarantines in the entire canton for this period, the article concludes that R/S=5% of all quarantines are due to SwissCovid. We can however observe that the ratio of cases which - they claim - are found thanks to SwissCovid is H/A=1.75%. The ratio between those two percentages shows that SwissCovid creates nearly 3 times more quarantines than overall methods to find cases. The burden of this task is put on people working at the infoline and health authorities: there are 756 (D) calls for 30 (H) cases in the end. We can observe that SwissCovid identified a population of 1418 (Q) under-risk people in which only 2% became cases to credit to SwissCovid, although the ratio of positive tests among all performed tests in Switzerland is around 15-20% at the moment. Also, alerting 1418 (Q) people for 30 (H) cases has a social cost which looks high. The adoption of SwissCovid is often used as an excuse for underperformances. Actually, the report concludes that increasing the adoption rate would increase the contribution of SwissCovid. Although it is true that H would increase, we can observe that the above performance indicators, the contribution to quarantines over the contribution to find cases (3) and the ratio of found cases per alerted people (2%), are independent of the adoption rate.

LotoCovid. An interesting idea is to design an app which would alert people at random, possibly using some biases but without using any exchanged information. Such app was ironically proposed on Twitter as LotoCovid by Gaëtan Leurent. If we alert 1418 (Q) people completely at random in Zurich, making them test would find A*Q/T=1.6 cases. This is twenty times lower than the score 30 (H) by SwissCovid but it is a much simpler app and infrastructure. Most likely, adding biases on the probability to alert (for instance, increasing the probability to clubers, to commuters, decreasing the probability to people living alone and teleworking) would certainly find many more cases by keeping the number of alerts the same. The performance of SwissCovid should seriously be compared to this trivial approach.

LotoCovid - Gaëtan Leurent

Possible Attacks

Several types of attacks against SwissCovid are possible. It is important for users to understand them. Indeed, SwissCovid is recognized as a medical device. Hence, the aware consent of users is fundamental. In normal medicine, awareness is guaranteed by providing the medicine together with objective information about possible side effects or observed risks. Having a clear and objective communication about the risks for taking SwissCovid can only strengthen trust.

Some attacks are easy to be done by anyone. Some of them are clearly illegal and people must not try them. For other attacks, which are based on collecting data, the legality is not clear at all because we are not sure the collected data are considered as personal data or not. Thus, we suggest not to try them.


Using a Bluetooth receiver (for instance, a phone), anyone can collect the ephemeral identifiers which are broadcasted around by phones. SwissCovid is not allowed to store the geolocalisation but other apps are not forbidden. Collecting identifier-time-location records allows to build a surveillance system. It can be enriched with the reported keys that anyone can collect from the server.

Here are a few attacks which are easy to mount. Welcome to the brave new world!

Those attacks are clear privacy threats to people. Users concerned about these should set/unset SwissCovid tracing responsibly.

Legal issues. We believe such attacks should be illegal. However, what they only collect is the ephemeral identifiers which are broadcasted over the air. (Own location and time belongs to whoever wants to store it.) If such data is not considered as personal data, such attack may be perfectly legal, which we find absurd.

The notion of personal information is actually a key issue here, because of the law on data protection. On the FOPH website we can read "The phone does not send any personal or location data to a central storage location or server". On another page we can read "The CDN only gives users access to information that cannot be used to obtain personal information (i.e. anonymous keys)". SwissCovid beams via Bluetooth some ephemeral identifiers which are derived (and can be recomputed) from keys. Diagnosed people post their keys on a publicly available server. Clearly, the position of SwissCovid promoters is that keys are anonymous. Anonymous information is not considered as personal information, thus not subject to regulation. If keys are anonymous, the identifiers which can be recomputed from them is anonymous too. Consequently, it is legal to make keys transit through a CDN, possibly abroad, without any regulation constraint. However, ephemeral identifiers become anonymous too hence no personal information. Therefore, they can be collected by anyone without regulation.

Instead, we believe that information which is exchanged via Bluetooth between phones and the one which is posted on the server are pseudonyms in the sense of the European regulation GDPR. They are not anonymous. Following GDPR, pseudonyms are private information. Hence, they should be subject to regulation on data protection. However, such recognition may have consequences on how the data stored on the server be regulated, and even on how phones collect ephemeral identifiers.

Extension with (other) personal data. We can of course improve the previous attacks by collecting more (personal) data. For instance, the Bluetooth surveillance can be enriched with a video-surveillance system so that ephemeral identifiers would link to a recorded video. If a reported key happens to generate an identifier which was collected, we can see the holder on the video, even though the contact was very brief and at distance.

An organization which identifies people can at the same time collect an ephemeral identifier and later one recognize if this person has become sick. This can be made at the reception of a hotel, at the cashier of a shop for people using a loyalty card, or at the entrance of a company for visitors or employee. Essentially, diagnosed people can be identified if they have even be seen by such surveillance system.

The basic attack was called the paparazzi attack in our April 8 report on DP3T. Essentially, a paparazzi can capture from far away one ephemeral identifier of a celebrity and regularly check on the server if this celebrity reported as diagnosed. Such information can be sold to tabloids. In a variant of this attack, a paparazzi can check which politicians are using SwissCovid and report. As acceptance of SwissCovid is becoming a political act, this could be a valuable information.

False-Alarm Injection

The ephemeral identifier of a person which is going to be diagnosed can be captured (even from far away) and maliciously be replayed somewhere else to simulate a risky encounter. This is likely to create a (false) at-risk notification. This way, we can get rid of some people by making them go to quarantine. According to specifications, an ephemeral identifier is supposed to be sent at some approximate time but the reception accepts it 2h before and 2h after. So, there is a lot of time to replay it.

In the (imaginary) lazy student attack, a student runs this attack on their classmates to send the entire class in quarantine and have an exam cancelled. Presumably, we can turn an event or an organization down in the same way.

In another type of attack, a group of people could use a malicious app which mimics the Bluetooth ephemeral identifier broadcasting but synchronize all devices on the same keys. The effect is that all those people would be considered as a single person. They can try to broadcast with high intensity to make a wide neighborhood think of a close encounter. If one person of the group is diagnosed, this can create a very high number of false at-risk notifications. This is a kind of terrorist attack which sends a huge number of people to quarantine.

Those attacks could exploit a problem in the GAEN implementation (namely, that the encrypted metadata in the Bluetooth signal is malleable). They could even be done from abroad, to escape from legal jurisdiction, by using sponsored add-ons in benign apps, as described in the following paper.

Finally, a more drastic attack could be to corrupt a diagnosed user to buy his covidcode, then meet the persons we want to send to quarantine, then use the covidcode to report our keys. A covidcode can be used only once, but it remains valid during 24 hours. Interestingly, a much easier attack was possible during the first three weeks of SwissCovid. FOPH was adding on the server 10 fake keys per day for maintenance reasons. However, the validity period of those keys was posterior to the date of insertion. Consequently, a malicious person could download them and use them during their validity period to simulate the broadcast of a diagnosed person. This problem was announced to be corrected on July 20.

The attack based on buying/selling covidcodes can be organized in a black market in a secure way (for the seller and the buyer). This can be automatized using the blockchain technology. A complete descriptions and analysis is provided in

Another option is to corrupt time, as described by Vincenzo Iovino. The adversary could replay identifiers derived from already reported keys on the server and send his victim to the past to receive those identifiers. Sending a phone to the past should be easy by corrupting the NTP protocol when the victim is connected to WiFi (for instance, when the adversary owns the WiFi network). When the victim is connected to the cellular network, it is also possible with a fake base station. We succeeded to send a phone to the past and receive a false alert. This was successfully tried for SwissCovid and its Italian relative Immuni as the following video explains.

Missed Goals

The DP3T White Paper lists a few goals of the project which are supposed to be implemented by SwissCovid. We list here the goals which have not been met.

Goal Meaning SwissCovid
Provide data to epidemiologists (This goal was originally present but was dropped in the May 25 version) Only the numbers of fake and genuine reports are available
Open-source Code available and runnable with changes Code not runnable, protocol in GAEN with partial/sample code
Decentralized Data stored on users's phone Data stored on Apple/Google distributed storage
Privacy-by-design Preserves the privacy of users Bluetooth technology creates severe privacy threats
Complete All proximity cases (<1.5m) are captured Some people living together do not seem to be captured
Precise Only proximity cases are captured Some far away cases (>1.5m) may be captured
Authentication Proximity cases cannot be faked Proximity cases can be faked

The first three goals are due to GAEN (we suspect providing data to epidemiologists was dropped because of being incompatible by the GAEN regulations to authorize the app). Completeness and precision are due to the well predicted hardness to measure distances with Bluetooth. The privacy loss is due to Bluetooth and to the DP3T decentralized architecture. The authentication problem is due to the DP3T scheme which is implemented by GAEN.

We collected encountered problems about SwissCovid in

So What?

There are many important factors to consider for the adoption of SwissCovid. For instance, the following report, issued from Armasuisse, considers the efficiency of the system for contact tracing, the battery usage, and the adoption likelihood in addition to security and privacy. This report compares various technologies and adopts a scoring scheme which is subjective. It concludes that the technology with the best average score is the one from SwissCovid. We can see however that the second best (with almost equal average score) is the technology which has the most terrible score for privacy protection. This illustrates that privacy may be a not-so-important factor.

We can wonder if the debate about privacy took the right direction. Of course, in countries with governments having too much power, privacy is a concern. But SwissCovid is deployed in Switzerland. In Switzerland, the SwissCovid approach on privacy consists of making sure that FOPH has no way to see who met whom or who activated SwissCovid from the data they collect. However, the health authority has legal means to investigate on contacts between people when it is needed. We can wonder if protecting against a possibly intrusive FOPH is making sense. At the same time, SwissCovid prevents FOPH from having fine grained access to epidemiological data or statistics. FOPH cannot see how many alerts SwissCovid raises and has little ways to measure the usefulness of the system. It does not say how SwissCovid is activated. Even worse: this architecture which is protecting against a corrupted FOPH is creating privacy issues which can come from any third party. In our April 8 report we already claimed that this dogmatic approach of decentralization was creating more privacy threats than it was solving.

Recently, Lanzing described three pitfalls related to ethics in digital contact tracing. The first common mistake is to associate privacy with anonymity. Privacy relates to the control on who uses people's data. For that, anonymity is neither necessary nor sufficient. Second, this technology currently means encouraging the monopoly of Apple and Google in framing digital public health. Finally, it creates some form of coercion by adding social pressure to adopt the technology, if not restrictions, and by excluding people who are already threatened by the digital divide.

Our concerns are about broken promises. SwissCovid was aimed to be transparent with open source but is not. SwissCovid was aimed to be privacy-preserving but is not. SwissCovid was aimed to be secure but is not. SwissCovid was aimed to be precise and complete but is not. Is it important enough to shut down SwissCovid? A big question is whether SwissCovid is useful at all. The law specifies that the Government should shut down SwissCovid if it appears that it is not efficient enough to fight against the pandemic LEp Art.60a al.8. We find abnormal that after three months of activities, there is still no reliable data, nor any established objective metric to measure the efficiency, nor any precision about what "efficient enough" would mean.

SwissCovid was developed and deployed in a rush, with the pressure of the pandemic. It is remarkable that SwissCovid was fully deployed so fast, but SwissCovid could have been much better with more time. Regarding GAEN, we are faced with fait accompli. The deal with Apple and Google could have been more careful. Those companies could have released a real interface giving access to Bluetooth instead of implementing the protocol by themselves. They could have released their source code too. (We reckon that some partial source code was released on July 21, which seems to move in the right direction.) SwissCovid could also have developed an app without GAEN as other countries did, but with battery issues. Other solutions in the protocol could have been investigated too. We listed several possible options in our May 6 report. For instance, there are ways to keep a decentralized flavor and to fix some privacy issues at the same time. For the next pandemic, we believe that more research should start now.

At the moment, there is no other option than SwissCovid. We can continue to beg Swiss residents to keep faith in that SwissCovid is or will be useful. However, we urge on establishing an objective usefulness metric and to open the debate on whether SwissCovid should be shut down.

Last update: November 11, 2020.