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Thali local P2P presence protocol for opportunistic synching

Informally defining the problem

Bob creates a post on his phone. The only way for Bob to communicate this post is via local radios (BLE, Bluetooth and Wi-Fi) because there is no Internet infrastructure in the neighborhood. In other words, all we have are mobile ad-hoc networks. Bob addressed the post to Alice, among others. So Bob wants to determine if Alice and the other people on the post are around and if so send them the post. The only way for Bob to figure this out is using his local radios.

The challenge is that Bob is being harassed by someone in his business group and he doesn’t want that person able to determine his location. In other words, he doesn’t ever want any of his local radios to give away his location to the person.

So how can Bob figure out if Alice and the others are in the area without leaking his identity?

This is just a specific example from a Bill of Rights I wrote up for people using P2P presence. P2P presence is an awesome feature but it’s scope for abuse is extensive. Only by designing our technologies to respect user rights can we hope to leverage P2P presence in a way that brings more benefit than harm.

Formally defining the problem

The previous description is nice but it leaves out some critical details of the problem. So I’m going to try to define the problem a bit more formally here.

A given device x has the following characteristics:

Kx: This is the public key that device x uses to identify its user

Lx: A set of devices, identified by their public key, that the user of device X is willing to expose their presence to. The value of Lx for any x is a secret known only to the device. Under no circumstances is Lx ever (EVER) shared between devices.

Mx: A set of devices, identified by their public keys, that Device X has been asked to synch data to.

Target Set: The target set is defined as Mx ∪ Lx. This is the set of devices that Device x has been asked to deliver to that it is willing to deliver to. Any device in Mx that is not in the target set will not be contacted by Device x.

There is a fixed period of time starting at T1 and ending at T2 during which a device will try to deliver any messages it has to any devices in the target set.

The challenge is that messages can only be delivered point to point. That is, if Device x has a message for Device y then Devices x and y must form a direct wireless connection to transfer the information.

Devices find each other using local discovery. Local discovery only works over a finite distance and has a very low bandwidth (in the case of BLE the maximum data rate is 35 KB/s and often much less). For the sake of this problem we will assume that it is impossible for two devices to identify each other just by their BLE signal. That is, we are constantly randomizing the BLE device address and we have sprinkled magic pixie dust to prevent radio fingerprinting. So the only way two devices can discover each other’s identity over local discovery is if they explicitly communicate it.

During the time window T2 - T1 a random set of devices will come within range of each other. There is no way to predict ahead of time who those devices will be and if any of them happen to be in a given device’s target set. In addition there is no reliable way before time T1 for all devices, or even the devices in a particular device’s target set to coordinate together. It’s possible, for example, for device x to have an entry in Mx for a device it’s literally never talked to before. This would happen, for example, if at some point before T1 Device x sent a message to Device y addressed to y and z. Device y has never seen Device z but now it has a message it needs to forward to z if it runs into that device.

The goal then is to figure out how to enable devices to discover if someone who is in their target set is within range without disclosing their identity to anyone who is not in the target set and without disclosing the contents of Lx beyond the obvious fact that if Device x and y talk then x must have been in Device y’s target set and y must have been in Device x’s target set.

Overview of Thali discovery for opportunistic synchronization

The base scenario for this specification is that there are two or more devices who are at least occasionally within radio range of each other but do not otherwise have a means to communicate directly. When one of these devices has information it would like to synch with another device then the first device will try to discover the second device. In other words, for any device x there is a target set of other devices Tx which consists of all the other devices that device x would like to discover and synchronize data with.

Device x will use whatever discovery mechanism is supported by its radios, this can be BLE, Wi-Fi Direct, Bluetooth, etc. Device x will create a discovery announcement which will consist of a version ID, a pre-amble and one or more beacons. One beacon for each member of Tx. In other words, device X isn’t going to announce its identity. Instead its going to announce, in a secure way, the identities of the devices it is looking for.

If device y, which is a member of Tx, receives device x’s discovery announcement, identifies itself in one of the beacons and decides that it wishes to respond to the discovery request then device y will contact device x and negotiate a connection. The exact details of how the connection is to be negotiated varies based on radio type and will be defined below.

Generating the pre-amble and beacons

A discovery announcement consists of three parts. A version Id, a pre-amble and one or more beacon values. How the version Id is transmitted is radio technology specific and will be defined below. This section of the document just defines the content of the pre-amble and beacon values which are the same regardless of transport.

The pre-amble and beacon are defined using Augmented BNF as specified in RFC 4234 with the exception that I changed the section 3.7 requirement on nRule to be n-Rule because 88OCTET look like 880 instead of 88:

DiscoveryAnnouncementWithoutVersionId = PreAmble 1*Beacon
PreAmble = PubKe Expiration
Expiration = 8-OCTET
PubKe = 88-OCTET
Beacon = 48-OCTET

PubKe MUST encode an Elliptic Curve Diffie-Hellman (ECDH) key using the secp256k1 curve. We use the secp256k1 curve, even though that curve is known to be slightly less secure than secp256r1, because the process for generating the curve is more open and thus less susceptible to subversion. To avoid potential patent issues we will transfer the key uncompressed using the X.509 SubjectPublicKeyInfo encoding as defined in RFC 5480. This means that a key that should probably have required (256/8 + 1) = 33 bytes to send will instead require 88 bytes.

NOTE: For the moment we plan on using the base64 encoded format outputted by Node.js’s ECDH library to encode the public keys. Eventually we’ll even figure out what format it is using and standardize that.

PubKe is an ephemeral public key that MUST be regenerated any time any part of the pre-amble or beacon list change.

OPEN ISSUE: If we at least used a direct binary encoding we could reduce the size to 66 bytes (no point compression) or 33 bytes with point compression. I didn’t do this because I wanted to use an encoding that was widely supported rather than create our own.

OPEN ISSUE: I am asserting that the X.509 encoding (with point compression explicitly disallowed) is always 88 bytes long. But I haven’t actually proven this.

Expiration MUST encode a 64 bit integer, in network byte order, specifying the number of milliseconds since the epoch (1/1/1970 at 00:00:00 GMT). The value in the Expiration MUST encode the point in time after which the sender does not want the discovery announcement to be honored. When generating the expiration value we MUST add a small random offset to the current system time in order to prevent attacks based on observing the device’s actual time. Note that it isn’t clear what the attack would be here but it’s easy to do.

Implementers MUST NOT honor discovery announcements with expirations that are too far into the future. It is up to each implementation to decide what constitutes “too far” but generally anything greater than 24 hours SHOULD be rejected.

Beacons are generated as given in the following pseudo-code:

function generateBeacons(setOfReceivingDevicesPublicKeys, Kx, IV, Ke, Expiration) {
  beacons = []
  UnencryptedKeyId = SHA256(Kx.public().encode()).first(16)

  for(PubKy : setOfReceivingDevicesPublicKeys) {
    Sey = ECDH(Ke.private(), PubKy)
    HKey = HKDF(SHA256, Sey, Expiration, 16)
    BeaconFlag = AESEncrypt(GCM, HKey, IV, 128, UnencryptedKeyId)
    Sxy = ECDH(Kx.private(), PubKy)
    HKxy = HKDF(SHA256, Sxy, Expiration, 32)
    BeaconHmac = HMAC(SHA256, HKxy, Expiration).first(16)

    beacons.append(BeaconFlag + BeaconHmac)
  return beacons

The variables used above are:

beacons - a byte array containing beacon content.

Expiration - a 64 bit integer encoding the desired expiration date for the discovery announcement measured in milliseconds since the epoch (1 January 1970 00:00:00 UTC).

GCM - Specifies Galois/Counter Mode for AES encryption.

Ke - an ephemeral key public/private key pair.

Kx - a public/private key pair for device x.

PubKy - a public key taken from setOfReceivingDevicesPublicKeys.

setOfReceivingDevicesPublicKeys - a set containing the public keys of the devices that the creator of the discovery announcement, device X, wants to synch with.

The functions used above are defined as follows:

AESEncrypt(mode, key, IV, GCMHashSizeInBits, value) - Returns the AES encryption of the value using the specified mode, GCM hash size in bits and key.

append(value) - Appends the given value to the array the function was called on. In this case we are appending a stream of bytes returned by the AES encryption.

ECDH(private key, public key) - Generates an ECDH shared secret using the given public key and private key (which are assumed to be from the same curve).

encode() - Returns a byte array with X.509 SubjectPublicKeyInfo encoding

first(length) - Returns the first length bytes of the array the function was called on.

HKDF(digest, IKM, salt, length) - Implements RFC 5869’s HKDF function using the specified digest, IKM and salt. It will then return length number of bytes of keying material.

HMAC(Digest, key, value) - Generates the HMAC of the specified value using the given digest and key.

public() - The public key of a public/private key pair

private() - The private key of a public/private key pair

SHA256(value) - Generates the SHA-256 digest of the given value.

slice(a, b) - Returns a sub-set of an array starting at byte a and end at the byte before b

+ - When applied to two arrays it concatenates them together.

Note that the security of the protocol rests, amongst other things, on us never encrypting the same value for the same peer at the same time. In general this guarantee is easy to keep because our code limits how often we update beacons to a time period greater than a millisecond which is our clock accuracy that we use for the expiration value. But it’s worth keeping in mind our dependence on the expiration value being unique when implementing the protocol. If we can’t guarantee that then we will need to introduce a nonce that is sent along with the beacons.

Processing the pre-amble and beacons

When a device receives a discovery announcement it has to validate several things.

If the device has seen the PubKe value previously then it MUST ignore the discovery announcement it has just received as a duplicate of a discovery announcement it has seen before. Therefore devices MUST have a cache to store PubKe values (or a secure hash therefore) they have seen before and SHOULD keep entries in the cache until the associated expiration time of the discovery announcement they were received on has passed. A should is specified versus a must in recognition of the fact that the cache may have to be purged due to space issues.

If a device hasn’t previously seen the PubKe then it MUST validate that the PubKe is from the appropriate curve or it MUST be ignored.

A device MUST also validate the expiration. If the expiration defines a time in the past or a time too far into the future then the receiver MUST ignore the discovery announcement.

The receiver then parses through the beacon values included in the announcement. Each beacon value is processed as follows:

function parseBeacons(beaconStream, addressBook, Ky, PubKe, Expiration) {
   while(beaconStream.empty() == false) {
    Beacon = beaconStream.read(48)
    Sey = ECDH(Ky.private, PubKe)
    KeyingMaterial = HKDF(SHA256, Sey, Expiration, 32)
    HKey = KeyingMaterial.slice(16, 32)
    EncryptedBeaconFlag = Beacon.slice(0, 32)
    UnencryptedKeyId = AESDecrypt(GCM, HKey, 0, 128, EncryptedBeaconFlag)

    if (UnencryptedKeyId == null) { // GCM mac check failed

    PubKx = addressBook.get(UnencryptedKeyId)

    if (PubKx == null) { // Device that claims to have sent the announcement is not recognized
      return null; // Once we find a matching beacon we stop, even if the sender is unrecognized

    BeaconHmac = Beacon.slice(32, 48)
    Sxy = ECDH(Ky.private(), PubKx)
    HKxy = HKDF(SHA256, Sxy, Expiration, 32)
    if (BeaconHmac.equals(HMAC(SHA256, HKxy, Expiration).first(16)) {
      return UnencryptedKeyId;
   return null;

Where variables or functions used above have the same names as in the previous section then they have the same functionality. Below are only listed variables and functions that do not appear above.

The new variables are:

addressBook - A dictionary object whose key is the SHA256 hash of the X.509 SubjectPublicKeyInfo serialization of a public key and whose value is an object representing that public key.

beaconStream - A byte stream containing all the beacons in a discovery announcement concatenated together.

Ky - The public/private key pair for the device parsing the discovery announcement.

PubKe - The de-serialized ephemeral public key from the discovery announcement.

The new functions are:

AESDecrypt(mode, key, GCMHashSizeInBits, value) - Returns the AES decryption of the value using the specified mode, GCM hash size in bits and key.

empty() - Specifies if a stream still has remaining content.

get(k) - Returns the value associated with the submitted key, null if there is no associated value.

read(n) - Reads n bytes from a stream.


Why can’t users just announce their keys publicly? Maybe encrypt them with a group key?

Looking at our informal definition we can imagine that one way for Bob to find Alice is for Alice to just announce her public key over BLE. In fact, if everyone would just announce their public keys over BLE everything would be so much simpler! Everyone can see exactly who is around, figure out if they have something for them and then transmit it. The problem is that now everyone is announcing their identity to anyone who will listen.

The usual work around at this point is some kind of group key. For example, imagine that what we announce is someone’s personal key but first we encrypt it with some group key that everyone at work has a copy of. This way locations will only be understandable to employees, nobody else will know who is announcing what.

The problem of course is that both Bob and his harasser are employees of the company and so the harasser will have the group key and be able to discover Bob.

There are variants on the group key approach but they tend to fail for reasons having to do both with the ad-hoc nature of communication in our scenario as well as for space reasons.

For example, imagine that Alice creates a new discussion group with Bob and they agree on a group key just for that discussion. Later on Alice decides to add Bob’s harasser to the discussion group and shares the group key with the harasser. Since this is an ad-hoc networking scenario there is absolutely no guarantee that Alice ever had the opportunity to tell Bob about this. And presumably Alice has no idea that Bob has put a ban on his harasser. So if Bob just looks for the group key, thinking it’s Alice, he could be inadvertently advertise his location to his harasser.

And, of course, our discovery channel is quite limited. So if there are lots and lots of groups there won’t be enough bandwidth to announce them all.

Code anyone?


The repro has a reference implementation of the discovery announcement. It is not as well tested as it should be but it gives the idea. It also contains a bunch of hacked together perf tests I used to learn how APIs worked and to test out different approaches. See the readme for details.


I started this work out just using HMAC-MD5 both because of size but more importantly because of performance. CPU is at a premium. I would also (naively) argue that most of the attacks on MD5 wouldn’t work properly in our scenario anyway because of the limited data size. But using SHA256 would make it easier for the crypto deities to be happy with us and it’s not really a size issue since we can just truncate the hash. But what about perf? Given that we are using the AES-GCM approach perf really isn’t that big a deal to be honest. We just aren’t using that many hashes. But for the ‘hot list’ approach the perf really matters since we have to do a lot of hashing.

So I ran some quick tests. In the first test I checked 20 hashes against an address book with 1000 keys where all comparisons fail. I repeated the test 100 times.

HMAC-MD5131 ms136 ms230 ms
HMAC-SHA256152 ms179.5 ms454 ms

For the next test I generated 20 HMACs using 20 different keys. This simulates creating beacons in an announcement.

HMAC-MD51 ms2 ms26 ms
HMAC-SHA2562 ms3 ms26 ms

The perf difference for generating tokens is noise. The slightly bigger issue is with validating them where using SHA256 looks to reduce performance by roughly 30%. That is small enough (and my tests are ham fisted enough) that I suspect the real world perf difference will be noise. So let’s make the crypto gods happy and just use SHA-256 with a truncated output to 128 bits to save space on the wire.

Comparing Addressbook Size Invariant Options - HMAC vs HMAC-AES-CBC vs AES-GCM

E(HMAC)xy = Ke + Timestamp + (HMAC-SHA256(Sey, Timestamp) + HMAC-SHA256(Sxy, Timestamp))+

E(HMAC-AES-CBC)xy = Ke + Timestamp + IV + (HMAC-SHA256(Sey, IV + Timestamp) + AES-CBC(Sey, SHA256(Kx) + HMAC-SHA256(Sxy, IV + Timestamp)))+

E(AES-GCM)xy = Ke + Timestamp + IV + (AES-GCM(Sey, SHA256(Kx) + HMAC-SHA256(Sxy, IV + Timestamp))+

I put the IV into the HMAC’s (where there was an IV) on the advice of Tolga Alcar who suggested this would add some additional randomness to the HMAC-SHA256 output. Since it’s cheap to do and doesn’t increase the size of the output this seemed reasonable.

Tolga also said that using the ECDH values (for Sey and Sxy) directly is a bad idea. That the output secret is effectively a point on the curve and that the is a non-uniform space (e.g. the points are predictably on the curve). He suggested looking up NIST SP800-108 and looking for the simplest possible hash based KDF and setting its counter to 1 and using that. Effectively just hashing the ECDH secret so that we map it into a uniform space from a non-uniform space. Bouncy Castle supports KDFCounterBytesGenerator so I can add that in for testing purposes. Later on I actually switched to HKDF mostly because I could actually read and understand its spec in a way that eluded me with the NIST document. It also claims to be more secure.

In each of the three options the first thing we have to do is use the ephemeral key with the receiver’s key to generate an Ellptic Curve Diffie Hellman key. For the performance test we used secp256k1 as our curve. We picked this solely because it was a 256 bit key thus providing roughly AES 128 equivalent strength and it was supported by bouncy castle on Android which was our test environment. The test device is a Nexus 7 phone running Android 4.4.4.

In each case the ECDH key is generated once regardless of how many identities are advertised in the presence announcement. That same key is then used to try to check each value. To test how expensive generating this ECDH key is I generated one key per test run and ran the test 1000 times. The cost to generate a single ECDH key was:

18 ms25 ms64 ms

So we know that right away the overhead for each of these approaches in time is around lets say 30 ms per announcement. Just keep in mind that it doesn’t matter how big the announcement is because this cost is fixed regardless of how many entries are in the announcement.

In the size table below we will ignore both Ke and Timestamp since they are the same for all options.

ApproachFixed Sized ElementSize for each announced identity
HMAC016 + 16 = 32 bytes
HMAC-AES-CBC1616 + (32 bytes unencrypted -> 48 bytes encrypted) = 64 bytes
AES-GCM1632 bytes unencrypted -> 48 bytes encrypted

To compare each approach I generated 20 entries in a single presence announcement. The last entry will match against the receiver. The receiver has an address book with 10,000 entries in it. The only reason for even specifying the number of entries is to give us a meaningful comparison between the cost of the HMAC approach versus everything else. We will intentionally match on the last entry in the address book to provide the worse case scenario. Each test was run 100 times except for the HMAC tests which I ran 10 times because I’m impatient.

HMAC/HMAC1691 ms1694 ms1741 ms
HMAC/AES-CBC22 ms36 ms73 ms
AES-GCM24 ms52 ms59 ms

What about ECIES?

E(ECIES)xh = Ke + Timestamp + IV + (ECIES(Sey, SHA256(Kx) + HMAC-SHA256(Sxy, IV + Timestamp))+

One can reasonably argue that AES-GCM and ECIES are morally equivalent. And I would expect them to produce outputs of comparable size and processing time.

But in practice that doesn’t seem to be the case. For example, the ECIES output I generate in my sample code is 133 bytes as opposed to the 48 bytes generated by AES-GCM. But I strongly suspect that this isn’t a fair comparison because I’m pretty sure my configuration for ECIES is wrong. I don’t think I’m using comparable hash functions or KDFs. My suspicion is that with the right configuration AES-GCM and ECIES would produce similar results.

But I have to admit my limits. I found hacking through the Bouncy Castle code really painful. Docs and examples are few and far between and out of date. I found one test file and one mail thread that even discuss ECIES. Not much else.

So my current thinking is basically that ECIES is not a standard, it’s not supported in the same way in different environments, the Bouncy Castle implementation is tricky and the AES-GCM approach seems to work well using widely available crypto primitives. So my plan is to bring both ECIES and AES-GCM to the crypto gurus at work and let them tell me what they think I should do.

Hot List

Exy = Timestamp + (HMAC-SHA256(Sxy, Timestamp))+

Imagine if someone has a relatively small address book. In that case someone could go through one time and generate Sxy for every person in the address book. Then when they receive a discovery announcement that just contains HMAC-SHA256 hashes of the timestamp they could very quickly check all the beacon values to see if they matched. I ran some tests (HashAndHmacTest.java/testCheckHashesAgainstAddressBook) and was able to check 20 hashes against an address book with 1000 entries in 131/187.5/596 (min/median/max). Which doesn’t sound great until you remember that most people, most of the time, won’t be working with more than say 10 or 20 people. So let’s imagine an address book that is based on Dunbar’s number (150). It’s what I call the “Hot List”. You would have your ‘full address book’ which could have an unlimited number of people. But you could also have a “hot list” of just people you frequently interact with. Those people could just send their hashes. We would only need 16 bytes per hash plus 8 bytes for the Timestamp. This would seriously shrink the size of our announcement. With the GCM approach announcing 20 identities requires 88 + 8 + (20 * 48) = 1056 bits. With a pure hash approach you would need 8 + (8 * 20) = 168 bits! That’s a massive difference. So running the same test with 150 people in the address book and 20 identities we get 20/28/99. That is essentially the same perf as the AES-GCM approach but with 6 times fewer bytes on the wire!

I did think about using Bloom Filters as well. In other words someone sending out an announcement using hashes could instead of including the hash just enter the hash into a bloom filter and send out the bloom filter. Then the person receiving the announcement could generate all the hashes and match them against the announced bloom filter. If I did the math right then a bloom filter that could hold 20 entries with a 1% false positive rate would need 192 or so bits. Which is longer than just sending out the hashes directly. I even tried a bloom filter that could hold the whole address book, say 150 entries with a 1% false positive rate. That required 1438 bits. Compare that to the hash which would be 8 + (8 * 150) = 1208 bits. So Bloom Filters only save space (at the cost of extra calculations) if we are willing to tolerate a higher error rate (which then wastes space due to unnecessary handshakes due to false positives). I’ll revisit this math later to make sure I didn’t screw anything up but it doesn’t look like bloom filters buy anything here.

As small as hot lists are there is a catch. If device X wants to find device Y and device X thinks it is on device Y’s hot list but is not then its hash won’t match even though device X might be in device Y’s address book. So devices have to know if they are on each other’s hot lists. And since hot lists have to have a maximum size there needs to be a way to get folks off the hot list. This means we need extra protocol cruft to let end points negotiate being on each other’s hot lists and we need at least some kind of expiration mechanism to automatically remove folks from the hot list after some window of time. None of this is hard but it’s just more stuff to do.

So my guess is that hot lists are a v3 optimization.

Transitive Attacks

It’s all nice and good to talk about protecting user privacy but we have to realize that in social networks protecting against other members of the network is somewhere between difficult to impossible. Our goal is to make harassers lives harder but realistically we cannot stop a determined enough harasser. For example, let’s say that Bob’s harasser is determined to be able to discovery Bob’s physical location anytime he is within a few hundred feet. The harasser will quickly be able to determine that they are blocked by Bob because any time the harasser sends out a message to Bob and Bob is within range the harasser will notice that their device doesn’t get connected to.

In fact dealing with blocking co-workers or members of the community is complicated enough that one suspects that in practice Bob won’t be able to use the filter list when his Thali app is in the foreground. That way if Bob and the harasser are working face to face then data will flow.

But once the harasser realizes they have been “de-friended” then the harasser still has options to discover Bob’s location. For example, the harasser probably knows that Bob works a lot with Alice and the harasser can reasonably guess that Alice isn’t blocked by Bob. In that case the harasser can create a message addressed to both Bob and Alice. Then the harasser can send the message directly to Alice. For bonus points the harasser can wait until Alice isn’t advertising any beacons. As soon as Alice gets the message from the harasser the first thing Alice’s phone will do is advertise a beacon for Bob in order to pass the message along. The harasser can now grab that token and use it to try to advertise for Bob.

We have put in some protections from this attack. For example, if Bob sees Alice’s token before the harasser sends it then Bob won’t honor the harasser’s stolen token because the token will already be in Bob’s cache. If, on the other hand, the harasser is able to use the token before Bob sees it from Alice then Bob will respond to it. No connection will be fully negotiated since the harasser would need Alice’s private key for that. But just the fact that a connection was made gives away Bob’s location. But remember, this only works once. Bob won’t respond to the same token again.

So the attacks aren’t super easy and they aren’t super effective but they absolutely are possible. It’s not clear that these kinds of attacks are even theoretically possible to stop in the absence of a mix or onion network. But mix/onion networks are not generally very usable in ad-hoc mobile networks with poor connectivity.

We still have obligations to do our best to protect users but we have to also recognize the limits of what is possible, especially in a social network where these sorts of transitive attacks are very possible.

And of course this is all without discussing the various IDs that Bob’s phone is already send out (see the last two sections here). So beware. There is no security panacea.

Are we guilty of bad public key hygiene?

The notification protocol and TLS binding require the use of a public key to identify both parties and then that public key is used directly for Diffie-Hellman key exchanges. In and of itself this isn’t bad. What’s bad is that currently those public keys are the root identity keys and so long lived. Typically good practice argues for having some kind of infrequently used root key which is then used to sign an intermediary key which is then used to sign a leaf key which is then used to sign an ephemeral key. Each key then rotates based on how low in the tree it is, the lower in the tree the more frequently it is changed. So we range from the root key at the top which is essentially invariant to the ephemeral key at the bottom which is changed on each use.

The problem with this approach is that we are both space and processor constrained. In an ideal world the beacon’s encrypted value would actually be encrypted using a key derived not from the sender’s root public key but instead from the ephemeral key and inside the encrypted statement would be a key chain where the ephemeral key would be signed by the leaf key which would be signed by the intermediate key which would then be signed by the root key. Unfortunately this approach would vastly (by orders of magnitude) increase the size of the beacon. So it’s not workable over the types of battery/bandwidth/process constrained environments we need to run in.

What’s interesting is that while this approach would protect the notifier’s root key from being over used, it would not protect the peer being notified. After all the root of the discovery mechanism is a Diffie-Hellman key exchange with the public key of the peer to be notified. There is no way for the notifier to know ahead of time what keys below the root the peer to be notified is currently using. So the notifier can’t use those keys in the Diffie-Hellman key exchange.

Another, probably much more significant, problem with using the root keys is that this means that the root identity keys have to be physically present on the device and usable by a process that is network connected. This is usually considered less than an ideal because it means a security compromise, much more likely with a network connected process, can be escalated into a full identity compromise. In an ideal work the root identity key would either not be on the device at all (the device only using a time limited key chain from the root authorizing it to act on the user’s behalf) or at the very least not on a process that is anywhere near a network connection.

My current best guess is that the way we will address these issues is by using purpose specific notification keys. These keys are not the root identity key but instead are separate keys that are generated and then signed by the root keys and exchanged during identity exchange. The keys are time limited and the peers would need to occasionally do exchanges of updated notification keys. In the case of personal meshes it’s even possible (although clearly not ideal as it puts too much of a burden on others and leaks too much information about the user’s devices) that each device in the mesh would have its own distinct notification keys. In that case if user A wants to notify user B who has 5 different devices then user A might have to advertise 5 different notification beacons, one for each of user B’s devices. Imagine replicating this across 100 users and we just increased the number of notification beacons by a factor of 5. But the alternative is that all the devices in the personal mesh have to share the same key which means moving the notification private key across the wire, generally a no-no.