dash/src/bloom.h

// Copyright (c) 2012-2019 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_BLOOM_H
#define BITCOIN_BLOOM_H

#include <serialize.h>
#include <span.h>

#include <vector>

class COutPoint;
class CScript;
class CTransaction;
class CTxOut;
class uint256;

//! 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;

    // Check matches for arbitrary script data elements
    bool CheckScript(const CScript& script) const;
    // Check particular CTxOut helper
    bool ProcessTxOut(const CTxOut& txout, const uint256& hash, unsigned int index);
    // Check additional matches for special transactions
    bool CheckSpecialTransactionMatchesAndUpdate(const CTransaction& tx);
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_BLOOM_H