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// Copyright (c) 2012-2020 The Bitcoin Core developers
// Distributed under the MIT software license, see the accompanying
// file COPYING or http://www.opensource.org/licenses/mit-license.php.
#ifndef BITCOIN_COMMON_BLOOM_H
#define BITCOIN_COMMON_BLOOM_H
#include <serialize.h>
#include <span.h>
#include <vector>
class COutPoint;
class CTransaction;
//! 20,000 items with fp rate < 0.1% or 10,000 items and <0.0001%
static constexpr unsigned int MAX_BLOOM_FILTER_SIZE = 36000; // bytes
static constexpr unsigned int MAX_HASH_FUNCS = 50;
/**
* First two bits of nFlags control how much IsRelevantAndUpdate actually updates
* The remaining bits are reserved
*/
enum bloomflags
{
BLOOM_UPDATE_NONE = 0,
BLOOM_UPDATE_ALL = 1,
// Only adds outpoints to the filter if the output is a pay-to-pubkey/pay-to-multisig script
BLOOM_UPDATE_P2PUBKEY_ONLY = 2,
BLOOM_UPDATE_MASK = 3,
};
/**
* BloomFilter is a probabilistic filter which SPV clients provide
* so that we can filter the transactions we send them.
*
* This allows for significantly more efficient transaction and block downloads.
*
* Because bloom filters are probabilistic, a SPV node can increase the false-
* positive rate, making us send it transactions which aren't actually its,
* allowing clients to trade more bandwidth for more privacy by obfuscating which
* keys are controlled by them.
*/
class CBloomFilter
{
private:
std::vector<unsigned char> vData;
unsigned int nHashFuncs;
unsigned int nTweak;
unsigned char nFlags;
unsigned int Hash(unsigned int nHashNum, Span<const unsigned char> vDataToHash) const;
public:
/**
* Creates a new bloom filter which will provide the given fp rate when filled with the given number of elements
* Note that if the given parameters will result in a filter outside the bounds of the protocol limits,
* the filter created will be as close to the given parameters as possible within the protocol limits.
* This will apply if nFPRate is very low or nElements is unreasonably high.
* nTweak is a constant which is added to the seed value passed to the hash function
* It should generally always be a random value (and is largely only exposed for unit testing)
* nFlags should be one of the BLOOM_UPDATE_* enums (not _MASK)
*/
CBloomFilter(const unsigned int nElements, const double nFPRate, const unsigned int nTweak, unsigned char nFlagsIn);
CBloomFilter() : nHashFuncs(0), nTweak(0), nFlags(0) {}
SERIALIZE_METHODS(CBloomFilter, obj) { READWRITE(obj.vData, obj.nHashFuncs, obj.nTweak, obj.nFlags); }
void insert(Span<const unsigned char> vKey);
void insert(const COutPoint& outpoint);
bool contains(Span<const unsigned char> vKey) const;
bool contains(const COutPoint& outpoint) const;
//! True if the size is <= MAX_BLOOM_FILTER_SIZE and the number of hash functions is <= MAX_HASH_FUNCS
//! (catch a filter which was just deserialized which was too big)
bool IsWithinSizeConstraints() const;
//! Also adds any outputs which match the filter to the filter (to match their spending txes)
bool IsRelevantAndUpdate(const CTransaction& tx);
};
/**
* RollingBloomFilter is a probabilistic "keep track of most recently inserted" set.
* Construct it with the number of items to keep track of, and a false-positive
* rate. Unlike CBloomFilter, by default nTweak is set to a cryptographically
* secure random value for you. Similarly rather than clear() the method
* reset() is provided, which also changes nTweak to decrease the impact of
* false-positives.
*
* contains(item) will always return true if item was one of the last N to 1.5*N
* insert()'ed ... but may also return true for items that were not inserted.
*
* It needs around 1.8 bytes per element per factor 0.1 of false positive rate.
* For example, if we want 1000 elements, we'd need:
* - ~1800 bytes for a false positive rate of 0.1
* - ~3600 bytes for a false positive rate of 0.01
* - ~5400 bytes for a false positive rate of 0.001
*
* If we make these simplifying assumptions:
* - logFpRate / log(0.5) doesn't get rounded or clamped in the nHashFuncs calculation
* - nElements is even, so that nEntriesPerGeneration == nElements / 2
*
* Then we get a more accurate estimate for filter bytes:
*
* 3/(log(256)*log(2)) * log(1/fpRate) * nElements
*/
class CRollingBloomFilter
{
public:
CRollingBloomFilter(const unsigned int nElements, const double nFPRate);
void insert(Span<const unsigned char> vKey);
bool contains(Span<const unsigned char> vKey) const;
void reset();
private:
int nEntriesPerGeneration;
int nEntriesThisGeneration;
int nGeneration;
std::vector<uint64_t> data;
unsigned int nTweak;
int nHashFuncs;
};
#endif // BITCOIN_COMMON_BLOOM_H
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