Pull out transaction related constants to the top of the test.
Adds a debug comment that is useful for this code. It is often needed to
check the human readable representation of a message.
We pass a random lnd connection identifier to the rule enforcer that is
unique per lnd connection lifetime. It is used to generate unique
request identifiers that amend the non-unique request identifiers that
are passed from lnd.
SessionDB is an interface that gives helper methods for how privacy
mapping should be done. A mock for SessionDB is added to save on some
code repetition, the privacy flags functionality is used in a later
test.
We obfuscate pubkeys, channel points and ids entered in configurations.
The channel id lengths for different block heights can be checked with:
```python
len(str(1 << 40 | 2923 << 16 | 30))
len(str(10_000_000 << 40 | 2923 << 16 | 30))
```
This commit adds a new FetchAllPairs to the PrivacyMapTx interface. This
method returns a new PrivacyMapPairs struct which is an in-memory
privacy map DB. The PrivacyMapPairs struct implements a new
PrivacyMapReader interface which can be used to pass around read only
access to the PrivacyMapPairs struct.
In this commit, in the response handlers of the privacy mapper, we
construct new response messages instead of overwriting existing messages
in order to minimize the chances of private data slipping through
accidentally.
Adds amount, timestamp, and channel initiator obfuscation to the two
response handlers `handleFwdHistoryResponse` and
`handleListChannelsResponse`.
In order to preserve privacy and still ensure functioning of algorithms
that rely on the randomized data, a trade-off between randomization and
accuracy needs to be found.
We choose ten minutes for forwarding timestamps as this breaks time
correlation of payments.
The amount obfuscation is chosen to be 5% and applies to the forwarding
amount and channel details to hide balances. We also remove details of
pending HTLCs in channels. Random obfuscation for amounts is chosen here
instead of rounding to have non-deterministic alteration of amounts,
which is especially important for forwardings to also break amount
correlation. Randomly varying around a certain value will statistically
skew averages less than rounding for algorithms that rely on
aggregation of individual data.
The privacy mapper is chosen to accept a randomness input in order to
ensure deterministic testing even when other handlers are changed in the
future.
Adds helper functions to randomize amounts, timestamps, and booleans.
Amounts are randomized based on a percentage and timestamps based on an
absolute scale.
