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    "text": " Hello everyone, thank you for attending my virtual presentation. So, I am Guy, I'm a master's student from EPFL and ETH Zurich in Switzerland, and I've been working with Arma Suisse on privacy in general aviation, and most particularly on the recent attempt from the FAA in the US. First, why do we need privacy in general aviation? In the recent years, a lot of articles underline privacy issues in general aviation. For instance, it would be possible to predict a company merchant acquisition by tracking a company aircraft, as demonstrated in the Bloomberg article. It is also possible to track diplomats' trips to a foreign country, which is of course not desirable, or to track the use of corporate checks. Furthermore, I guess that pilots just don't want to be tracked when flying, so that's why a privacy policy is needed in general aviation. But before diving into the actual policy, I will quickly introduce aircraft communications. Aircraft need to be identifiable at all times when flying for obvious security reasons. So, first it can be identified by the tail number, which is the unique registration by the national authority, it's also called N number in the US, and this number can never be changed. So, that is the number that is usually printed at the back of the aircraft or on the tail. Aircraft can also be identified by their ICAO address, which is a 24-bit transponder ID, that is assigned by the International Civil Aviation Organization. And there is a direct translation between the tail number and the ICAO address in the US. So, if you're interested, I wrote a converter that is available on my GitHub. And aircraft can also be identified by their callsign, which is a unique flight identifier, and it can be changed for each flight. And, for instance, private aircraft often use their tail number as a callsign. Aircraft use ADS-B, standing for Automatic Dependent Surveillance Broadcast, to communicate with the ground station and with other aircraft. ADS-B equipment is mandatory in many countries, such as the US, Australia, and European countries. I will not explain in detail how ADS-B operates, but what we need to know is that, aircraft broadcast twice-a-second information, such as their position, velocity, etc. And these broadcasts are identified by the ICAO address and the callsign of the aircraft. Anyone equipped with a cheap software-defined radio can receive these broadcasts from a distance of 600 kilometers. Crowdsource networks try to cover large areas with antenna and software-defined radios, in order to get a global ADS-B coverage and make aircraft trajectories publicly available online. Using a crowdsource network, it is trivial to track an aircraft using only its ICAO address or its callsign. Aircraft can change their callsign for each flight, but not their ICAO address, so they are actually easy to track. So, this situation is a serious issue for aircraft and passenger privacy. Until recently, the only way aircraft operators could protect themselves from being tracked, was to ask kindly crowdsource networks to make tracking unavailable for the aircraft. But some crowdsource networks do not offer this service. Concerning these issues, the FAA published the following statement. FAA acknowledges the desire of some operators to limit the availability of real-time ADS-B position and identification information for a specific aircraft. To address privacy concerns, the FAA has initiated the Privacy ICAO Address Program to improve the privacy of eligible aircraft. So, this Privacy ICAO Address Program allows enrolled aircraft operators to periodically change their ICAO address, acting as a pseudonym. So, if the aircraft operator simultaneously changes their ICAO address and the callsign of the aircraft, it makes the aircraft harder to track. This program is unfortunately, at least for now, limited to aircraft registered in the US, using a specific ADS-B system and a third-party callsign, just for flights in the domestic US airspace. Which means that if an aircraft wants to fly abroad, it has to use its permanently assigned ICAO address and is automatically revealed to anyone that wishes to track it. In the first phase of the program, the FAA will monitor the program, and a new Privacy ICAO Address, or PIA, can be requested every 60 calendar days. And in the second phase, the program will be transitioned to third-party callsign providers, and the PIA change frequency will go down to 20 business days, or 28 calendar days, approximately. To illustrate the privacy improvement, we show an aircraft identified by its ICAO address landing at an airport. On ground, at the airport, the operator will request a new Privacy ICAO address and program it into the aircraft transponder and change the aircraft callsign before its next flight. So an adversary can observe that an aircraft with ICAO address A12345 arrived at the airport, and that another aircraft with ICAO address is ABCDEF left the airport later on. If the aircraft is the only one that stay at the airport for the given time frame, then identifying that A12345 and ABCDEF is actually the same aircraft is trivial. But if multiple aircraft enrolled in the PIA program are changing their ICAO address in the same airport during the same time period, it is not possible to link accurately aircraft arriving at the airport with aircraft leaving the airport. So the best attack is in fact to take a random guess. So in order to maximize the privacy of aircraft using this scheme, we need to maximize the number of aircraft using a PIA and using the same callsign provider changing their PIA simultaneously at the very same airport. So that's a lot of conditions, a lot of parameters that can influence the privacy level, and we're going to discuss that in details. So here is the bigger picture of the ideal system where all aircraft change their identifier for each flight without any side channel information. So a global adversary can observe the flights, but it is hard to link accurately a flight from a given aircraft. You cannot distinguish aircraft when they are flying if they change their ICAO address and callsign at each stop. So this results actually in an asynchronous free route mixed net where all aircraft stay inside the mixed net forever. In order to quantify privacy performance of such a system, we will look at its traceability index which is defined as the expected ratio of successfully tracked aircraft over time. Here is an example. Time is represented on the x-axis and traceability index of the system is represented on the y-axis. And the traceability index of this example system after 150 days is 50.3 percent. From the beginning of the year, we observed the US airspace through the crowdsource network OpenSky networks and detected that 16 aircraft are using a PIA address. Nine of them are using a DCM callsign and seven of them are using a FFL callsign. Those are two distinct sets of aircraft. We observed aircraft changing their IKO address to a PIA and some of them didn't even update their callsign. So that's trivial to track them even if they wanted to enroll to this privacy program. So we did not observe any PIA change although some operators have been using the PIA for much more than 60 days. We observed that all IKO addresses that we suspected to be PIAs are in the N number range from N41000 to N42. And that 1062 official FIA registration looked like the one on the right. So that is reserved with no fee on the 10-03-2019 by the SBS program office. And I will now explain and attack on how to track aircraft enrolled in the PIA program. So the first step is obviously to identify a target aircraft enrolled in this program. Then we need to associate the privacy IKO address with an actual aircraft registration. And finally we need to monitor every PIA change of this aircraft. To detect an aircraft using a PIA we first look for flights using an IKO address in this given range. So the hexadecimal range. And then we check in the FIA registry if the associated N number is reserved with no fee by the SBN program office. And if it is the case then we're almost sure that this aircraft is using a PIA. After we identify the target aircraft we need to find its original registration. To reach that goal we need to find the very first flight where the privacy IKO address PIA1 was used. So that is our target PIA. So in our example as the flight departs from Chicago we need to find all aircraft that landed at the very same airport in Chicago before the departure of the red flight. So these aircraft are the candidates and we need to eliminate all of them but one. For instance after the departure of the red flight an aircraft which is also identified by the address IKO1 is observed leaving Chicago. So this aircraft is eliminated. Then the aircraft identified by the IKO address IKO2 is observed flying back to its origin. So there is only one candidate left which is our target aircraft that changed its IKO address from IKO3 to PIA1. So we detected the IKO address change. Once we know IKO3 we look for the associated N number. So we do the translation and we look for the associated N number in the FIA registry to find the actual aircraft registration containing all of the owner's details like the address and everything. And so once we have the actual aircraft registration we only need to monitor the PIA change which is essentially the same operation as described previously. Note that if multiple aircraft are changing their PIA at the same airport during the same time period we have to select one at random and the tracking may be inaccurate. But it is also possible to observe flying patterns of the possible candidates to increase the probability to select the correct aircraft according to its pattern after the change. We built a simulator to help us predict how the PIA program would scale if significantly more aircraft joined it. This simulator takes as parameter a number of aircraft, a number of airports, the average aircraft flight frequency, the PIA change frequency and also the simulation duration. So once the simulation starts an aircraft will be picked at random and will fly to a random airport. And then another aircraft would be chosen at random flying to another airport and so on. So as the trajectories are random the simulator is not totally realistic but it provides a lower bound to the traceability index as a random flight maximizes the entropy of the system. The simulator is available on github if you want to check it out. And we implemented the previously described attack to show the performance when we make varied simulator parameters. On this graph we see the traceability index curve for a set of 200 aircraft and 100 are over a year where aircraft update their PIA respectively every 60 calendar days as is phase one of the program so that's the green curve. 20 business days which is 28 calendar days so that's the orange curve for phase two of the program and every 10 day in blue that is just to show difference. So we see that after a year the traceability index of the 60 days frequency update is at 30 percent whereas the score is the same score is reached after only 101 days and 37 days for the 28 and 10 days PIA update frequency. So updating the aircraft PIA as often as possible gets the best privacy for the system. And now we make the aircraft fleet size vary while keeping the PIA update frequency to 28 days. So a fleet is a set of aircraft using a PIA and the same cosine space so all of the aircraft are using a DCM cosine or all of them are using a FFL cosine. So we go from a fleet of 50 aircraft in blue to a fleet of 500 aircraft in red and we see that the traceability index after 150 days go from 87.9 percent for the 50 aircraft fleet down to only 1.5 percent for the 500 aircraft fleet. So these curves really show that maximizing the number of aircraft for a fixed set of airports and minimizes the traceability index of the system which is what we want. Some obvious improvement to the PIA program would be to add an international pole IC so that aircraft do not need to use their permanently assigned ICAO address when flying abroad because that reveals their position but also which PIA they used in the past and so it would be easier to track all of their flight history until the moment they flew abroad. And it would be also good to make all ADSP equipped aircraft eligible to enroll in this program in order to maximize the the number of aircraft. And all aircraft using a PIA should use the same cosine range which is not the case currently as you can use the program with a DCM and FFL cosine so that makes two distinct sets of aircraft and one for each cosine providers. As the privacy is maximized when aircraft change their PIA as often as possible, a major improvement to the program would be to allow aircraft to update their PIA for each flight. This gives us the best theoretical privacy performance without modifying aircraft trajectories. But it comes at a price so it introduces extra work for the administration that has to keep track of all PIA changes and it takes extra effort for aircraft operator to program the new PIA before each flight. So on this graph we can see the traceability index reached by a PIA change for each flight in green compared to the traceability index of the program in phase one and two in orange and blue. And we can see that the so the current PIA program is still very far from the best possible performance. Another major possible improvement is to make all aircraft change their PIA simultaneously. This method would consist in making all aircraft owner update their PIA on or before the first flight after a given day. This would help maximizing the number of aircraft updating their PIA during the same time period at the same airport as they all update their PIA at the same time. This strategy would cause no extra cost for the administration as there would be the exact same number of changes and could even be adopted without any official changes in the program if for instance a group of aircraft operators agree on the dates at which they will update their PIA. The drawback of this method is that privacy performance is still far from the theoretical maximum but it's still better than in the regular PIA policy. So this graph represents the traceability index curve of the 28 days update policy with the PIA update uniformly distributed in blue. 28 days policy where all aircraft update their PIA simultaneously in orange and in green the best theoretical performance where their aircraft change their PIA for each flight. So we observe a major improvement for the simulation update policy compared with the uniformly distributed one. For instance after 150 days the traceability index goes from 56.9 percent in the regular PIA down to 31.3 in the enhanced version. To conclude this presentation we showed that the PIA program makes it slightly harder to track aircraft using ADS-B data from crowdsourced networks. It just makes it more annoying to retrace from a privacy IKO address back to the aircraft owner but it's still possible so in a sense the PIA program does not really meet its privacy goal. We show with the simulator that even with a much larger number of aircraft using a PIA we can still track a large proportion of them over time even if they update their PIA as often as possible. And we propose two concrete solutions to improve the privacy in the PIA program. So if you want more details about our work please make sure to check out our paper when it's out. And finally my take-home message is if you get to use this program please make sure to update your privacy IKO address as often as possible to increase your privacy level and others privacy level."
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