Initial Vexta Core v0.1.0 release

src/bloom.h

@@ -0,0 +1,132 @@
+// Copyright (c) 2012-2020 The Bitcoin Core developers
+// Copyright (c) 2014-2020 The DigiByte Core developers
+// Distributed under the MIT software license, see the accompanying
+// file COPYING or http://www.opensource.org/licenses/mit-license.php.
+
+#ifndef DIGIBYTE_BLOOM_H
+#define DIGIBYTE_BLOOM_H
+
+#include <serialize.h>
+
+#include <vector>
+
+class COutPoint;
+class CTransaction;
+class uint256;
+
+//! 20,000 items with fp rate < 0.1% or 10,000 items and <0.0001%
+static const unsigned int MAX_BLOOM_FILTER_SIZE = 36000; // bytes
+static const 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, const std::vector<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(const std::vector<unsigned char>& vKey);
+    void insert(const COutPoint& outpoint);
+    void insert(const uint256& hash);
+
+    bool contains(const std::vector<unsigned char>& vKey) const;
+    bool contains(const COutPoint& outpoint) const;
+    bool contains(const uint256& hash) 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(const std::vector<unsigned char>& vKey);
+    void insert(const uint256& hash);
+    bool contains(const std::vector<unsigned char>& vKey) const;
+    bool contains(const uint256& hash) const;
+
+    void reset();
+
+private:
+    int nEntriesPerGeneration;
+    int nEntriesThisGeneration;
+    int nGeneration;
+    std::vector<uint64_t> data;
+    unsigned int nTweak;
+    int nHashFuncs;
+};
+
+#endif // DIGIBYTE_BLOOM_H