Nick Mathewson
2017-12-01 20:26:19 UTC
Filename: 288-privcount-with-shamir.txt Title: Privacy-Preserving Statistics
with Privcount in Tor (Shamir version) Author: Nick Mathewson, Tim
Wilson-Brown, Aaron Johnson Created: 1-Dec-2017 Supercedes: 280 Status: Draft
0. Acknowledgments
Tariq Elahi, George Danezis, and Ian Goldberg designed and implemented the
PrivEx blinding scheme. Rob Jansen and Aaron Johnson extended PrivEx's
differential privacy guarantees to multiple counters in PrivCount:
https://github.com/privcount/privcount/blob/master/README.markdown#research-background
Rob Jansen and Tim Wilson-Brown wrote the majority of the experimental
PrivCount code, based on the PrivEx secret-sharing variant. This
implementation includes contributions from the PrivEx authors, and others:
https://github.com/privcount/privcount/blob/master/CONTRIBUTORS.markdown
This research was supported in part by NSF grants CNS-1111539, CNS-1314637,
CNS-1526306, CNS-1619454, and CNS-1640548.
The use of a Shamir secret-sharing-based approach is due to a suggestion by
Aaron Johnson (iirc); Carolin Zöbelein did some helpful analysis here.
Aaron Johnson and Tim Wilson-Brown made improvements to the draft proposal.
1. Introduction and scope
PrivCount is a privacy-preserving way to collect aggregate statistics about
the Tor network without exposing the statistics from any single Tor relay.
This document describes the behavior of the in-Tor portion of the PrivCount
system. It DOES NOT describe the counter configurations, or any other
parts of the system. (These will be covered in separate proposals.)
2. PrivCount overview
Here follows an oversimplified summary of PrivCount, with enough
information to explain the Tor side of things. The actual operation of the
non-Tor components is trickier than described below.
In PrivCount, a Data Collector (DC, in this case a Tor relay) shares
numeric data with N different Tally Reporters (TRs). (A Tally Reporter
performs the summing and unblinding roles of the Tally Server and Share
Keeper from experimental PrivCount.)
All N Tally Reporters together can reconstruct the original data, but no
(N-1)-sized subset of the Tally Reporters can learn anything about the
data.
(In reality, the Tally Reporters don't reconstruct the original data at
all! Instead, they will reconstruct a _sum_ of the original data across all
participating relays.)
In brief, the system works as follow:
To share data, for each counter value V to be shared, the Data Collector
first adds Gaussian noise to V in order to produce V', uses (K,N) Shamir
secret-sharing to generate N shares of V' (K<=N, K being the reconstruction
threshold), encrypts each share to a different Tally Reporter, and sends
each encrypted share to the Tally Reporter it is encrypted for.
The Tally Reporters then agree on the set S of Data Collectors that sent
data to all of them, and each Tally Reporter forms a share of the aggregate
value by decrypting the shares it received from the Data Collectors in S
and adding them together. The Tally Reporters then, collectively, perform
secret reconstruction, thereby learning the sum of all the different values
V'.
The use of Shamir secret sharing lets us survive up to N-K crashing TRs.
Waiting until the end to agree on a set S of surviving relays lets us
survive an arbitrary number of crashing DCs. In order to prevent bogus data
from corrupting the tally, the Tally Reporters can perform the aggregation
step multiple times, each time proceeding with a different subset of S and
taking the median of the resulting values.
Relay subsets should be chosen at random to avoid relays manipulating their
subset membership(s). If an shared random value is required, all relays
must submit their results, and then the next revealed shared random value
can be used to select relay subsets. (Tor's shared random value can be
calculated as soon as all commits have been revealed. So all relay results
must be received *before* any votes are cast in the reveal phase for that
shared random value.)
Below we describe the algorithm in more detail, and describe the data
format to use.
3. The algorithm
All values below are B-bit integers modulo some prime P; we suggest B=62
and P = 2**62 - 2**30 - 1 (hex 0x3fffffffbfffffff). The size of this field
is an upper limit on the largest sum we can calculate; it is not a security
parameter.
There are N Tally Reporters: every participating relay must agree on which
N exist, and on their current public keys. We suggest listing them in the
consensus networkstatus document. All parties must also agree on some
ordering the Tally Reporters. Similarly, all parties must also agree on
some value K<=N.
There are a number of well-known "counters", identified known by ASCII
identifiers. Each counter is a value that the participating relays will
know how to count. Let C be the number of counters.
3.1. Data Collector (DC) side
At the start of each period, every Data Collector ("client" below)
initializes their state as follows
1. For every Tally Reporter with index i, the client constructs a
random 32-byte random value SEED_i. The client then generates a
pseudorandom bitstream of C*B bits using the SHAKE-256 XOF with
SEED_i as its input, and divides this stream into C values, with the
c'th value denoted by MASK(i, c).
[Because P is very close to a power of 2, nearly all seeds will
produce MASK values in range 0...(P-1). If any does not, the client
picks a new seed.]
2. The client encrypts SEED_i using the public key of Tally Reporter i,
and remembers this encrypted value. It discards SEED_i.
3. For every counter c, the client generates a noise value Z_c from an
appropriate Gaussian distribution. If the noise value is negative,
the client adds P to bring Z_c into the range 0...(P-1). (The noise
MUST be sampled using the procedure in Appendix C.)
The client then uses Shamir secret sharing to generate N shares
(x,y) of Z_c, 1 <= x <= N, with the x'th share to be used by the
x'th Tally Reporter. See Appendix A for more on Shamir secret
sharing. See Appendix B for another idea about X coordinates.
The client picks a random value CTR_c and stores it in the counter,
which serves to locally blind the counter.
The client then subtracts (MASK(x, c)+CTR_c) from y, giving
"encrypted shares" of (x, y0) where y0 = y-CTR_c.
The client then discards all MASK values, all CTR values, and all
original shares (x,y), all CTR and the noise value Z_c. For each
counter c, it remembers CTR_c, and N shares of the form (x, y).
To increment a counter by some value "inc":
1. The client adds "inc" to counter value, modulo P.
(This step is chosen to be optimal, since it will happen more
frequently than any other step in the computation.)
Aggregate counter values that are close to P/2 MUST be scaled to
avoid overflow. See Appendix D for more information. (We do not
think that any counters on the current Tor network will require
scaling.)
To publish the counter values:
1. The client publishes, in the format described below:
The list of counters it knows about The list of TRs it knows about
For each TR: For each counter c: A list of (i, y-CTR_c-MASK(x,c)),
which corresponds to the share for the i'th TR of counter c. SEED_i
as encrypted earlier to the i'th TR's public key.
3.2. Tally Reporter (TR) side
This section is less completely specified than the Data Collector's
behavior: I expect that the TRs will be easier to update as we proceed.
(Each TR has a long-term identity key (ed25519). It also has a sequence of
short-term curve25519 keys, each associated with a single round of data
collection.)
1. When a group of TRs receives information from the Data Collectors, they
collectively chose a set S of DCs and a set of counters such that every
TR in the group has a valid entry for every counter, from every DC in
the set.
To be valid, an entry must not only be well-formed, but must also have
the x coordinate in its shares corresponding to the TR's position in
the list of TRs.
2. For each Data Collector's report, the i'th TR decrypts its part of the
client's report using its curve25519 key. It uses SEED_i and SHAKE-256
to regenerate MASK(0) through MASK(C-1). Then for each share (x,
y-CTR_c-MASK(x,c)) (note that x=i), the TR reconstructs the true share
of the value for that DC and counter c by adding V+MASK(x,c) to the y
coordinate to yield the share (x, y_final).
3. For every counter in the set, each TR computes the sum of the y_final
values from all clients.
4. For every counter in the set, each TR publishes its a share of the sum
as (x, SUM(y_final)).
5. If at least K TRs publish correctly, then the sum can be reconstructed
using Lagrange polynomial interpolation. (See Appendix A).
6. If the reconstructed sum is greater than P/2, it is probably a negative
value. The value can be obtained by subtracting P from the sum.
(Negative values are generated when negative noise is added to small
signals.)
7. If scaling has been applied, the sum is scaled by the scaling factor.
(See Appendix D.)
4. The document format
4.1. The counters document.
This document format builds on the line-based directory format used for
other tor documents, described in Tor's dir-spec.txt.
Using this format, we describe a "counters" document that publishes the
shares collected by a given DC, for a single TR.
The "counters" document has these elements:
"privctr-dump-format" SP VERSION SP SigningKey
[At start, exactly once]
Describes the version of the dump format, and provides an ed25519
signing key to identify the relay. The signing key is encoded in
base64 with padding stripped. VERSION is "alpha" now, but should be
"1" once this document is finalized.
"starting-at" SP IsoTime
[Exactly once]
The start of the time period when the statistics here were collected.
"ending-at" SP IsoTime
[Exactly once]
The end of the time period when the statistics here were collected.
"share-parameters" SP Number SP Number
[Exactly once]
The number of shares needed to reconstruct the client's measurements
(K), and the number of shares produced (N), respectively.
"tally-reporter" SP Identifier SP Integer SP Key
[At least twice]
The curve25519 public key of each Tally Reporter that the relay
believes in. (If the list does not match the list of participating
Tally Reporters, they won't be able to find the relay's values
correctly.) The identifiers are non-space, non-nul character
sequences. The Key values are encoded in base64 with padding
stripped; they must be unique within each counters document. The
Integer values are the X coordinate of the shares associated with each
Tally Reporter.
"encrypted-to-key" SP Key
[Exactly once]
The curve25519 public key to which the report below is encrypted.
Note that it must match one of the Tally Reporter options above.
"report" NL "----- BEGIN ENCRYPTED MESSAGE-----" NL Base64Data "----- END
ENCRYPTED MESSAGE-----" NL
[Exactly once]
An encrypted document, encoded in base64. The plaintext format is
described in section 4.2. below. The encryption is as specified in
section 5 below, with STRING_CONSTANT set to "privctr-shares-v1".
"signature" SP Signature
[At end, exactly once]
The Ed25519 signature of all the fields in the document, from the
first byte, up to but not including the "signature" keyword here. The
signature is encoded in base64 with padding stripped.
4.2. The encrypted "shares" document.
The shares document is sent, encrypted, in the "report" element above. Its
plaintext contents include these fields:
"encrypted-seed" NL "----- BEGIN ENCRYPTED MESSAGE-----" NL Base64Data
"----- END ENCRYPTED MESSAGE-----" NL
[At start, exactly once.]
An encrypted document, encoded in base64. The plaintext value is the
32-byte value SEED_i for this TR. The encryption is as specified in
section 5 below, with STRING_CONSTANT set to "privctr-seed-v1".
"d" SP Keyword SP Integer
[Any number of times]
For each counter, the name of the counter, and the obfuscated Y
coordinate of this TR's share for that counter. (The Y coordinate is
calculated as y-CTR_c as in 3.1 above.) The order of counters must
correspond to the order used when generating the MASK() values;
different clients do not need to choose the same order.
5. Hybrid encryption
This scheme is taken from rend-spec-v3.txt, section 2.5.3, replacing
"secret_input" and "STRING_CONSTANT". It is a hybrid encryption method
for encrypting a message to a curve25519 public key PK.
We generate a new curve25519 keypair (sk,pk).
We run the algorithm of rend-spec-v3.txt 2.5.3, replacing "secret_input"
with Curve25519(sk,PK) | SigningKey, where SigningKey is the DC's
signing key. (Including the DC's SigningKey here prevents one DC from
replaying another one's data.)
We transmit the encrypted data as in rend-spec-v3.txt 2.5.3, prepending
pk.
Appendix A. Shamir secret sharing for the impatient
In Shamir secret sharing, you want to split a value in a finite field into
N shares, such that any K of the N shares can reconstruct the original
value, but K-1 shares give you no information at all.
The key insight here is that you can reconstruct a K-degree polynomial
given K+1 distinct points on its curve, but not given K points.
So, to split a secret, we going to generate a (K-1)-degree polynomial.
We'll make the Y intercept of the polynomial be our secret, and choose all
the other coefficients at random from our field.
Then we compute the (x,y) coordinates for x in [1, N]. Now we have N
points, any K of which can be used to find the original polynomial.
Moreover, we can do what PrivCount wants here, because adding the y
coordinates of N shares gives us shares of the sum: If P1 is the
polynomial made to share secret A and P2 is the polynomial made to share
secret B, and if (x,y1) is on P1 and (x,y2) is on P2, then (x,y1+y2) will
be on P1+P2 ... and moreover, the y intercept of P1+P2 will be A+B.
To reconstruct a secret from a set of shares, you have to either go learn
about Lagrange polynomials, or just blindly copy a formula from your
favorite source.
Here is such a formula, as pseudocode^Wpython, assuming that each share is
an object with a _x field and a _y field.
def interpolate(shares): for sh in shares: product_num = FE(1)
product_denom = FE(1) for sh2 in shares: if sh2 is sh: continue
product_num *= sh2._x product_denom *= (sh2._x - sh._x)
accumulator += (sh._y * product_num) / product_denom
return accumulator
Appendix B. An alternative way to pick X coordinates
Above we describe a system where everybody knows the same TRs and puts
them in the same order, and then does Shamir secret sharing using "x" as
the x coordinate for the x'th TR.
But what if we remove that requirement by having x be based on a hash of
the public key of the TR? Everything would still work, so long as all
users chose the same K value. It would also let us migrate TR sets a
little more gracefully.
Appendix C. Sampling floating-point Gaussian noise for differential privacy
Background:
When we add noise to a counter value (signal), we want the added noise to
protect all of the bits in the signal, to ensure differential privacy.
But because noise values are generated from random double(s) using
floating-point calculations, the resulting low bits are not distributed
evenly enough to ensure differential privacy.
As implemented in the C "double" type, IEEE 754 double-precision
floating-point numbers contain 53 significant bits in their mantissa. This
means that noise calculated using doubles can not ensure differential
privacy for client activity larger than 2**53: * if the noise is scaled to
the magnitude of the signal using multiplication, then the low bits are
unprotected, * if the noise is not scaled, then the high bits are
unprotected.
But the operations in the noise transform also suffer from floating-point
inaccuracy, further affecting the low bits in the mantissa. So we can only
protect client activity up to 2**46 with Laplacian noise. (We assume that
the limit for Gaussian noise is similar.)
Our noise generation procedure further reduces this limit to 2**42. For
byte counters, 2**42 is 4 Terabytes, or the observed bandwidth of a 1 Gbps
relay running at full speed for 9 hours. It may be several years before we
want to protect this much client activity. However, since the mitigation
is relatively simple, we specify that it MUST be implemented.
Procedure:
Data collectors MUST sample noise as follows: 1. Generate random double(s)
in [0, 1] that are integer multiples of 2**-53. TODO: the Gaussian
transform in step 2 may require open intervals 2. Generate a Gaussian
floating-point noise value at random with sigma 1, using the random
double(s) generated in step 1. 3. Multiply the floating-point noise by
the floating-point sigma value. 4. Truncate the scaled noise to an
integer to remove the fractional bits. (These bits can never correspond
to signal bits, because PrivCount only collects integer counters.)
5. If the floating-point sigma value from step 3 is large enough that
any noise value could be greater than or equal to 2**46, we need to
randomise the low bits of the integer scaled noise value. (This ensures
that the low bits of the signal are always hidden by the noise.)
If we use the sample_unit_gaussian() transform in nickm/privcount_nm:
A. The maximum r value is sqrt(-2.0*ln(2**-53)) ~= 8.57, and the
maximal sin(theta) values are +/- 1.0. Therefore, the generated noise
values can be greater than or equal to 2**46 when the sigma value is
greater than 2**42. B. Therefore, the number of low bits that need
to be randomised is: N = floor(sigma / 2**42) C. We randomise the
lowest N bits of the integer noise by replacing them with a uniformly
distributed N-bit integer value in 0...(2**N)-1. 6. Add the integer
noise to the integer counter, before the counter is incremented in
response to events. (This ensures that the signal value is always
protected.)
This procedure is security-sensitive: changing the order of
multiplications, truncations, or bit replacements can expose the low or
high bits of the signal or noise.
As long as the noise is sampled using this procedure, the low bits of the
signal are protected. So we do not need to "bin" any signals.
The impact of randomising more bits than necessary is minor, but if we
fail to randomise an unevenly distributed bit, client activity can be
exposed. Therefore, we choose to randomise all bits that could
potentially be affected by floating-point inaccuracy.
Justification:
Although this analysis applies to Laplacian noise, we assume a similar
analysis applies to Gaussian noise. (If we add Laplacian noise on DCs, the
total ends up with a Gaussian distribution anyway.)
TODO: check that the 2**46 limit applies to Gaussian noise.
This procedure results in a Gaussian distribution for the higher ~42 bits
of the noise. We can safely ignore the value of the lower bits of the
noise, because they are insignificant for our reporting.
This procedure is based on section 5.2 of: "On Significance of the Least
Significant Bits For Differential Privacy" Ilya Mironov, ACM CCS 2012
https://www.microsoft.com/en-us/research/wp-content/uploads/2012/10/lsbs.pdf
We believe that this procedure is safe, because we neither round nor
smooth the noise values. The truncation in step 4 has the same effect as
Mironov's "safe snapping" procedure. Randomising the low bits removes the
2**46 limit on the sigma value, at the cost of departing slightly from the
ideal infinite-precision Gaussian distribution. (But we already know that
these bits are distributed poorly, due to floating-point inaccuracy.)
Mironov's analysis assumes that a clamp() function is available to clamp
large signal and noise values to an infinite floating-point value.
Instead of clamping, PrivCount's arithmetic wraps modulo P. We believe
that this is safe, because any reported values this large will be
meaningless modulo P. And they will not expose any client activity,
because "modulo P" is an arithmetic transform of the summed noised signal
value.
Alternatives:
We could round the encrypted value to the nearest multiple of the
unprotected bits. But this relies on the MASK() value being a uniformly
distributed random value, and it is less generic.
We could also simply fail when we reach the 2**42 limit on the sigma
value, but we do not want to design a system with a limit that low.
We could use a pure-integer transform to create Gaussian noise, and avoid
floating-point issues entirely. But we have not been able to find an
efficient pure-integer Gaussian or Laplacian noise transform. Nor do we
know if such a transform can be used to ensure differential privacy.
Appendix D. Scaling large counters
We do not believe that scaling will be necessary to collect PrivCount
statistics in Tor. As of November 2017, the Tor network advertises a
capacity of 200 Gbps, or 2**51 bytes per day. We can measure counters as
large as ~2**61 before reaching the P/2 counter limit.
If scaling becomes necessary, we can scale event values (and noise sigmas)
by a scaling factor before adding them to the counter. Scaling may
introduce a bias in the final result, but this should be insignificant for
reporting.
Appendix Z. Remaining client-side uncertainties
[These are the uncertainties at the client side. I'm not considering
TR-only operations here unless they affect clients.]
Should we do a multi-level thing for the signing keys? That is, have an
identity key for each TR and each DC, and use those to sign short-term
keys?
How to tell the DCs the parameters of the system, including: - who the TRs
are, and what their keys are? - what the counters are, and how much noise
to add to each? - how do we impose a delay when the noise parameters
change? (this delay ensures differential privacy even when the old and
new counters are compared) - or should we try to monotonically increase
counter noise? - when the collection intervals start and end? - what
happens in networks where some relays report some counters, and other
relays report other counters? - do we just pick the latest counter
version, as long as enough relays support it? (it's not safe to report
multiple copies of counters)
How the TRs agree on which DCs' counters to collect?
How data is uploaded to DCs?
What to say about persistence on the DC side?
with Privcount in Tor (Shamir version) Author: Nick Mathewson, Tim
Wilson-Brown, Aaron Johnson Created: 1-Dec-2017 Supercedes: 280 Status: Draft
0. Acknowledgments
Tariq Elahi, George Danezis, and Ian Goldberg designed and implemented the
PrivEx blinding scheme. Rob Jansen and Aaron Johnson extended PrivEx's
differential privacy guarantees to multiple counters in PrivCount:
https://github.com/privcount/privcount/blob/master/README.markdown#research-background
Rob Jansen and Tim Wilson-Brown wrote the majority of the experimental
PrivCount code, based on the PrivEx secret-sharing variant. This
implementation includes contributions from the PrivEx authors, and others:
https://github.com/privcount/privcount/blob/master/CONTRIBUTORS.markdown
This research was supported in part by NSF grants CNS-1111539, CNS-1314637,
CNS-1526306, CNS-1619454, and CNS-1640548.
The use of a Shamir secret-sharing-based approach is due to a suggestion by
Aaron Johnson (iirc); Carolin Zöbelein did some helpful analysis here.
Aaron Johnson and Tim Wilson-Brown made improvements to the draft proposal.
1. Introduction and scope
PrivCount is a privacy-preserving way to collect aggregate statistics about
the Tor network without exposing the statistics from any single Tor relay.
This document describes the behavior of the in-Tor portion of the PrivCount
system. It DOES NOT describe the counter configurations, or any other
parts of the system. (These will be covered in separate proposals.)
2. PrivCount overview
Here follows an oversimplified summary of PrivCount, with enough
information to explain the Tor side of things. The actual operation of the
non-Tor components is trickier than described below.
In PrivCount, a Data Collector (DC, in this case a Tor relay) shares
numeric data with N different Tally Reporters (TRs). (A Tally Reporter
performs the summing and unblinding roles of the Tally Server and Share
Keeper from experimental PrivCount.)
All N Tally Reporters together can reconstruct the original data, but no
(N-1)-sized subset of the Tally Reporters can learn anything about the
data.
(In reality, the Tally Reporters don't reconstruct the original data at
all! Instead, they will reconstruct a _sum_ of the original data across all
participating relays.)
In brief, the system works as follow:
To share data, for each counter value V to be shared, the Data Collector
first adds Gaussian noise to V in order to produce V', uses (K,N) Shamir
secret-sharing to generate N shares of V' (K<=N, K being the reconstruction
threshold), encrypts each share to a different Tally Reporter, and sends
each encrypted share to the Tally Reporter it is encrypted for.
The Tally Reporters then agree on the set S of Data Collectors that sent
data to all of them, and each Tally Reporter forms a share of the aggregate
value by decrypting the shares it received from the Data Collectors in S
and adding them together. The Tally Reporters then, collectively, perform
secret reconstruction, thereby learning the sum of all the different values
V'.
The use of Shamir secret sharing lets us survive up to N-K crashing TRs.
Waiting until the end to agree on a set S of surviving relays lets us
survive an arbitrary number of crashing DCs. In order to prevent bogus data
from corrupting the tally, the Tally Reporters can perform the aggregation
step multiple times, each time proceeding with a different subset of S and
taking the median of the resulting values.
Relay subsets should be chosen at random to avoid relays manipulating their
subset membership(s). If an shared random value is required, all relays
must submit their results, and then the next revealed shared random value
can be used to select relay subsets. (Tor's shared random value can be
calculated as soon as all commits have been revealed. So all relay results
must be received *before* any votes are cast in the reveal phase for that
shared random value.)
Below we describe the algorithm in more detail, and describe the data
format to use.
3. The algorithm
All values below are B-bit integers modulo some prime P; we suggest B=62
and P = 2**62 - 2**30 - 1 (hex 0x3fffffffbfffffff). The size of this field
is an upper limit on the largest sum we can calculate; it is not a security
parameter.
There are N Tally Reporters: every participating relay must agree on which
N exist, and on their current public keys. We suggest listing them in the
consensus networkstatus document. All parties must also agree on some
ordering the Tally Reporters. Similarly, all parties must also agree on
some value K<=N.
There are a number of well-known "counters", identified known by ASCII
identifiers. Each counter is a value that the participating relays will
know how to count. Let C be the number of counters.
3.1. Data Collector (DC) side
At the start of each period, every Data Collector ("client" below)
initializes their state as follows
1. For every Tally Reporter with index i, the client constructs a
random 32-byte random value SEED_i. The client then generates a
pseudorandom bitstream of C*B bits using the SHAKE-256 XOF with
SEED_i as its input, and divides this stream into C values, with the
c'th value denoted by MASK(i, c).
[Because P is very close to a power of 2, nearly all seeds will
produce MASK values in range 0...(P-1). If any does not, the client
picks a new seed.]
2. The client encrypts SEED_i using the public key of Tally Reporter i,
and remembers this encrypted value. It discards SEED_i.
3. For every counter c, the client generates a noise value Z_c from an
appropriate Gaussian distribution. If the noise value is negative,
the client adds P to bring Z_c into the range 0...(P-1). (The noise
MUST be sampled using the procedure in Appendix C.)
The client then uses Shamir secret sharing to generate N shares
(x,y) of Z_c, 1 <= x <= N, with the x'th share to be used by the
x'th Tally Reporter. See Appendix A for more on Shamir secret
sharing. See Appendix B for another idea about X coordinates.
The client picks a random value CTR_c and stores it in the counter,
which serves to locally blind the counter.
The client then subtracts (MASK(x, c)+CTR_c) from y, giving
"encrypted shares" of (x, y0) where y0 = y-CTR_c.
The client then discards all MASK values, all CTR values, and all
original shares (x,y), all CTR and the noise value Z_c. For each
counter c, it remembers CTR_c, and N shares of the form (x, y).
To increment a counter by some value "inc":
1. The client adds "inc" to counter value, modulo P.
(This step is chosen to be optimal, since it will happen more
frequently than any other step in the computation.)
Aggregate counter values that are close to P/2 MUST be scaled to
avoid overflow. See Appendix D for more information. (We do not
think that any counters on the current Tor network will require
scaling.)
To publish the counter values:
1. The client publishes, in the format described below:
The list of counters it knows about The list of TRs it knows about
For each TR: For each counter c: A list of (i, y-CTR_c-MASK(x,c)),
which corresponds to the share for the i'th TR of counter c. SEED_i
as encrypted earlier to the i'th TR's public key.
3.2. Tally Reporter (TR) side
This section is less completely specified than the Data Collector's
behavior: I expect that the TRs will be easier to update as we proceed.
(Each TR has a long-term identity key (ed25519). It also has a sequence of
short-term curve25519 keys, each associated with a single round of data
collection.)
1. When a group of TRs receives information from the Data Collectors, they
collectively chose a set S of DCs and a set of counters such that every
TR in the group has a valid entry for every counter, from every DC in
the set.
To be valid, an entry must not only be well-formed, but must also have
the x coordinate in its shares corresponding to the TR's position in
the list of TRs.
2. For each Data Collector's report, the i'th TR decrypts its part of the
client's report using its curve25519 key. It uses SEED_i and SHAKE-256
to regenerate MASK(0) through MASK(C-1). Then for each share (x,
y-CTR_c-MASK(x,c)) (note that x=i), the TR reconstructs the true share
of the value for that DC and counter c by adding V+MASK(x,c) to the y
coordinate to yield the share (x, y_final).
3. For every counter in the set, each TR computes the sum of the y_final
values from all clients.
4. For every counter in the set, each TR publishes its a share of the sum
as (x, SUM(y_final)).
5. If at least K TRs publish correctly, then the sum can be reconstructed
using Lagrange polynomial interpolation. (See Appendix A).
6. If the reconstructed sum is greater than P/2, it is probably a negative
value. The value can be obtained by subtracting P from the sum.
(Negative values are generated when negative noise is added to small
signals.)
7. If scaling has been applied, the sum is scaled by the scaling factor.
(See Appendix D.)
4. The document format
4.1. The counters document.
This document format builds on the line-based directory format used for
other tor documents, described in Tor's dir-spec.txt.
Using this format, we describe a "counters" document that publishes the
shares collected by a given DC, for a single TR.
The "counters" document has these elements:
"privctr-dump-format" SP VERSION SP SigningKey
[At start, exactly once]
Describes the version of the dump format, and provides an ed25519
signing key to identify the relay. The signing key is encoded in
base64 with padding stripped. VERSION is "alpha" now, but should be
"1" once this document is finalized.
"starting-at" SP IsoTime
[Exactly once]
The start of the time period when the statistics here were collected.
"ending-at" SP IsoTime
[Exactly once]
The end of the time period when the statistics here were collected.
"share-parameters" SP Number SP Number
[Exactly once]
The number of shares needed to reconstruct the client's measurements
(K), and the number of shares produced (N), respectively.
"tally-reporter" SP Identifier SP Integer SP Key
[At least twice]
The curve25519 public key of each Tally Reporter that the relay
believes in. (If the list does not match the list of participating
Tally Reporters, they won't be able to find the relay's values
correctly.) The identifiers are non-space, non-nul character
sequences. The Key values are encoded in base64 with padding
stripped; they must be unique within each counters document. The
Integer values are the X coordinate of the shares associated with each
Tally Reporter.
"encrypted-to-key" SP Key
[Exactly once]
The curve25519 public key to which the report below is encrypted.
Note that it must match one of the Tally Reporter options above.
"report" NL "----- BEGIN ENCRYPTED MESSAGE-----" NL Base64Data "----- END
ENCRYPTED MESSAGE-----" NL
[Exactly once]
An encrypted document, encoded in base64. The plaintext format is
described in section 4.2. below. The encryption is as specified in
section 5 below, with STRING_CONSTANT set to "privctr-shares-v1".
"signature" SP Signature
[At end, exactly once]
The Ed25519 signature of all the fields in the document, from the
first byte, up to but not including the "signature" keyword here. The
signature is encoded in base64 with padding stripped.
4.2. The encrypted "shares" document.
The shares document is sent, encrypted, in the "report" element above. Its
plaintext contents include these fields:
"encrypted-seed" NL "----- BEGIN ENCRYPTED MESSAGE-----" NL Base64Data
"----- END ENCRYPTED MESSAGE-----" NL
[At start, exactly once.]
An encrypted document, encoded in base64. The plaintext value is the
32-byte value SEED_i for this TR. The encryption is as specified in
section 5 below, with STRING_CONSTANT set to "privctr-seed-v1".
"d" SP Keyword SP Integer
[Any number of times]
For each counter, the name of the counter, and the obfuscated Y
coordinate of this TR's share for that counter. (The Y coordinate is
calculated as y-CTR_c as in 3.1 above.) The order of counters must
correspond to the order used when generating the MASK() values;
different clients do not need to choose the same order.
5. Hybrid encryption
This scheme is taken from rend-spec-v3.txt, section 2.5.3, replacing
"secret_input" and "STRING_CONSTANT". It is a hybrid encryption method
for encrypting a message to a curve25519 public key PK.
We generate a new curve25519 keypair (sk,pk).
We run the algorithm of rend-spec-v3.txt 2.5.3, replacing "secret_input"
with Curve25519(sk,PK) | SigningKey, where SigningKey is the DC's
signing key. (Including the DC's SigningKey here prevents one DC from
replaying another one's data.)
We transmit the encrypted data as in rend-spec-v3.txt 2.5.3, prepending
pk.
Appendix A. Shamir secret sharing for the impatient
In Shamir secret sharing, you want to split a value in a finite field into
N shares, such that any K of the N shares can reconstruct the original
value, but K-1 shares give you no information at all.
The key insight here is that you can reconstruct a K-degree polynomial
given K+1 distinct points on its curve, but not given K points.
So, to split a secret, we going to generate a (K-1)-degree polynomial.
We'll make the Y intercept of the polynomial be our secret, and choose all
the other coefficients at random from our field.
Then we compute the (x,y) coordinates for x in [1, N]. Now we have N
points, any K of which can be used to find the original polynomial.
Moreover, we can do what PrivCount wants here, because adding the y
coordinates of N shares gives us shares of the sum: If P1 is the
polynomial made to share secret A and P2 is the polynomial made to share
secret B, and if (x,y1) is on P1 and (x,y2) is on P2, then (x,y1+y2) will
be on P1+P2 ... and moreover, the y intercept of P1+P2 will be A+B.
To reconstruct a secret from a set of shares, you have to either go learn
about Lagrange polynomials, or just blindly copy a formula from your
favorite source.
Here is such a formula, as pseudocode^Wpython, assuming that each share is
an object with a _x field and a _y field.
def interpolate(shares): for sh in shares: product_num = FE(1)
product_denom = FE(1) for sh2 in shares: if sh2 is sh: continue
product_num *= sh2._x product_denom *= (sh2._x - sh._x)
accumulator += (sh._y * product_num) / product_denom
return accumulator
Appendix B. An alternative way to pick X coordinates
Above we describe a system where everybody knows the same TRs and puts
them in the same order, and then does Shamir secret sharing using "x" as
the x coordinate for the x'th TR.
But what if we remove that requirement by having x be based on a hash of
the public key of the TR? Everything would still work, so long as all
users chose the same K value. It would also let us migrate TR sets a
little more gracefully.
Appendix C. Sampling floating-point Gaussian noise for differential privacy
Background:
When we add noise to a counter value (signal), we want the added noise to
protect all of the bits in the signal, to ensure differential privacy.
But because noise values are generated from random double(s) using
floating-point calculations, the resulting low bits are not distributed
evenly enough to ensure differential privacy.
As implemented in the C "double" type, IEEE 754 double-precision
floating-point numbers contain 53 significant bits in their mantissa. This
means that noise calculated using doubles can not ensure differential
privacy for client activity larger than 2**53: * if the noise is scaled to
the magnitude of the signal using multiplication, then the low bits are
unprotected, * if the noise is not scaled, then the high bits are
unprotected.
But the operations in the noise transform also suffer from floating-point
inaccuracy, further affecting the low bits in the mantissa. So we can only
protect client activity up to 2**46 with Laplacian noise. (We assume that
the limit for Gaussian noise is similar.)
Our noise generation procedure further reduces this limit to 2**42. For
byte counters, 2**42 is 4 Terabytes, or the observed bandwidth of a 1 Gbps
relay running at full speed for 9 hours. It may be several years before we
want to protect this much client activity. However, since the mitigation
is relatively simple, we specify that it MUST be implemented.
Procedure:
Data collectors MUST sample noise as follows: 1. Generate random double(s)
in [0, 1] that are integer multiples of 2**-53. TODO: the Gaussian
transform in step 2 may require open intervals 2. Generate a Gaussian
floating-point noise value at random with sigma 1, using the random
double(s) generated in step 1. 3. Multiply the floating-point noise by
the floating-point sigma value. 4. Truncate the scaled noise to an
integer to remove the fractional bits. (These bits can never correspond
to signal bits, because PrivCount only collects integer counters.)
5. If the floating-point sigma value from step 3 is large enough that
any noise value could be greater than or equal to 2**46, we need to
randomise the low bits of the integer scaled noise value. (This ensures
that the low bits of the signal are always hidden by the noise.)
If we use the sample_unit_gaussian() transform in nickm/privcount_nm:
A. The maximum r value is sqrt(-2.0*ln(2**-53)) ~= 8.57, and the
maximal sin(theta) values are +/- 1.0. Therefore, the generated noise
values can be greater than or equal to 2**46 when the sigma value is
greater than 2**42. B. Therefore, the number of low bits that need
to be randomised is: N = floor(sigma / 2**42) C. We randomise the
lowest N bits of the integer noise by replacing them with a uniformly
distributed N-bit integer value in 0...(2**N)-1. 6. Add the integer
noise to the integer counter, before the counter is incremented in
response to events. (This ensures that the signal value is always
protected.)
This procedure is security-sensitive: changing the order of
multiplications, truncations, or bit replacements can expose the low or
high bits of the signal or noise.
As long as the noise is sampled using this procedure, the low bits of the
signal are protected. So we do not need to "bin" any signals.
The impact of randomising more bits than necessary is minor, but if we
fail to randomise an unevenly distributed bit, client activity can be
exposed. Therefore, we choose to randomise all bits that could
potentially be affected by floating-point inaccuracy.
Justification:
Although this analysis applies to Laplacian noise, we assume a similar
analysis applies to Gaussian noise. (If we add Laplacian noise on DCs, the
total ends up with a Gaussian distribution anyway.)
TODO: check that the 2**46 limit applies to Gaussian noise.
This procedure results in a Gaussian distribution for the higher ~42 bits
of the noise. We can safely ignore the value of the lower bits of the
noise, because they are insignificant for our reporting.
This procedure is based on section 5.2 of: "On Significance of the Least
Significant Bits For Differential Privacy" Ilya Mironov, ACM CCS 2012
https://www.microsoft.com/en-us/research/wp-content/uploads/2012/10/lsbs.pdf
We believe that this procedure is safe, because we neither round nor
smooth the noise values. The truncation in step 4 has the same effect as
Mironov's "safe snapping" procedure. Randomising the low bits removes the
2**46 limit on the sigma value, at the cost of departing slightly from the
ideal infinite-precision Gaussian distribution. (But we already know that
these bits are distributed poorly, due to floating-point inaccuracy.)
Mironov's analysis assumes that a clamp() function is available to clamp
large signal and noise values to an infinite floating-point value.
Instead of clamping, PrivCount's arithmetic wraps modulo P. We believe
that this is safe, because any reported values this large will be
meaningless modulo P. And they will not expose any client activity,
because "modulo P" is an arithmetic transform of the summed noised signal
value.
Alternatives:
We could round the encrypted value to the nearest multiple of the
unprotected bits. But this relies on the MASK() value being a uniformly
distributed random value, and it is less generic.
We could also simply fail when we reach the 2**42 limit on the sigma
value, but we do not want to design a system with a limit that low.
We could use a pure-integer transform to create Gaussian noise, and avoid
floating-point issues entirely. But we have not been able to find an
efficient pure-integer Gaussian or Laplacian noise transform. Nor do we
know if such a transform can be used to ensure differential privacy.
Appendix D. Scaling large counters
We do not believe that scaling will be necessary to collect PrivCount
statistics in Tor. As of November 2017, the Tor network advertises a
capacity of 200 Gbps, or 2**51 bytes per day. We can measure counters as
large as ~2**61 before reaching the P/2 counter limit.
If scaling becomes necessary, we can scale event values (and noise sigmas)
by a scaling factor before adding them to the counter. Scaling may
introduce a bias in the final result, but this should be insignificant for
reporting.
Appendix Z. Remaining client-side uncertainties
[These are the uncertainties at the client side. I'm not considering
TR-only operations here unless they affect clients.]
Should we do a multi-level thing for the signing keys? That is, have an
identity key for each TR and each DC, and use those to sign short-term
keys?
How to tell the DCs the parameters of the system, including: - who the TRs
are, and what their keys are? - what the counters are, and how much noise
to add to each? - how do we impose a delay when the noise parameters
change? (this delay ensures differential privacy even when the old and
new counters are compared) - or should we try to monotonically increase
counter noise? - when the collection intervals start and end? - what
happens in networks where some relays report some counters, and other
relays report other counters? - do we just pick the latest counter
version, as long as enough relays support it? (it's not safe to report
multiple copies of counters)
How the TRs agree on which DCs' counters to collect?
How data is uploaded to DCs?
What to say about persistence on the DC side?