scripted-diff: Move bloom to src/common

-BEGIN VERIFY SCRIPT-
 # Move to directory
 mkdir                src/common
 git mv src/bloom.cpp src/common/
 git mv src/bloom.h   src/common/

 # Replace occurrences
 sed -i 's|\<bloom\.cpp\>|common/bloom.cpp|g' $(git grep -l 'bloom.cpp')
 sed -i 's|\<bloom\.h\>|common/bloom.h|g'     $(git grep -l 'bloom.h')
 sed -i 's|BITCOIN_BLOOM_H|BITCOIN_COMMON_BLOOM_H|g' $(git grep -l 'BLOOM_H')
-END VERIFY SCRIPT-

2 files changed, 380 insertions, 0 deletions

diff --git a/src/common/bloom.cpp b/src/common/bloom.cpp
new file mode 100644
index 0000000000..26b70b4d14
--- /dev/null
+++ b/src/common/bloom.cpp
@@ -0,0 +1,253 @@
+// 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.
+
+#include <common/bloom.h>
+
+#include <hash.h>
+#include <primitives/transaction.h>
+#include <random.h>
+#include <script/script.h>
+#include <script/standard.h>
+#include <span.h>
+#include <streams.h>
+
+#include <algorithm>
+#include <cmath>
+#include <cstdlib>
+#include <limits>
+#include <vector>
+
+static constexpr double LN2SQUARED = 0.4804530139182014246671025263266649717305529515945455;
+static constexpr double LN2 = 0.6931471805599453094172321214581765680755001343602552;
+
+CBloomFilter::CBloomFilter(const unsigned int nElements, const double nFPRate, const unsigned int nTweakIn, unsigned char nFlagsIn) :
+    /**
+     * The ideal size for a bloom filter with a given number of elements and false positive rate is:
+     * - nElements * log(fp rate) / ln(2)^2
+     * We ignore filter parameters which will create a bloom filter larger than the protocol limits
+     */
+    vData(std::min((unsigned int)(-1  / LN2SQUARED * nElements * log(nFPRate)), MAX_BLOOM_FILTER_SIZE * 8) / 8),
+    /**
+     * The ideal number of hash functions is filter size * ln(2) / number of elements
+     * Again, we ignore filter parameters which will create a bloom filter with more hash functions than the protocol limits
+     * See https://en.wikipedia.org/wiki/Bloom_filter for an explanation of these formulas
+     */
+    nHashFuncs(std::min((unsigned int)(vData.size() * 8 / nElements * LN2), MAX_HASH_FUNCS)),
+    nTweak(nTweakIn),
+    nFlags(nFlagsIn)
+{
+}
+
+inline unsigned int CBloomFilter::Hash(unsigned int nHashNum, Span<const unsigned char> vDataToHash) const
+{
+    // 0xFBA4C795 chosen as it guarantees a reasonable bit difference between nHashNum values.
+    return MurmurHash3(nHashNum * 0xFBA4C795 + nTweak, vDataToHash) % (vData.size() * 8);
+}
+
+void CBloomFilter::insert(Span<const unsigned char> vKey)
+{
+    if (vData.empty()) // Avoid divide-by-zero (CVE-2013-5700)
+        return;
+    for (unsigned int i = 0; i < nHashFuncs; i++)
+    {
+        unsigned int nIndex = Hash(i, vKey);
+        // Sets bit nIndex of vData
+        vData[nIndex >> 3] |= (1 << (7 & nIndex));
+    }
+}
+
+void CBloomFilter::insert(const COutPoint& outpoint)
+{
+    CDataStream stream(SER_NETWORK, PROTOCOL_VERSION);
+    stream << outpoint;
+    insert(stream);
+}
+
+bool CBloomFilter::contains(Span<const unsigned char> vKey) const
+{
+    if (vData.empty()) // Avoid divide-by-zero (CVE-2013-5700)
+        return true;
+    for (unsigned int i = 0; i < nHashFuncs; i++)
+    {
+        unsigned int nIndex = Hash(i, vKey);
+        // Checks bit nIndex of vData
+        if (!(vData[nIndex >> 3] & (1 << (7 & nIndex))))
+            return false;
+    }
+    return true;
+}
+
+bool CBloomFilter::contains(const COutPoint& outpoint) const
+{
+    CDataStream stream(SER_NETWORK, PROTOCOL_VERSION);
+    stream << outpoint;
+    return contains(stream);
+}
+
+bool CBloomFilter::IsWithinSizeConstraints() const
+{
+    return vData.size() <= MAX_BLOOM_FILTER_SIZE && nHashFuncs <= MAX_HASH_FUNCS;
+}
+
+bool CBloomFilter::IsRelevantAndUpdate(const CTransaction& tx)
+{
+    bool fFound = false;
+    // Match if the filter contains the hash of tx
+    //  for finding tx when they appear in a block
+    if (vData.empty()) // zero-size = "match-all" filter
+        return true;
+    const uint256& hash = tx.GetHash();
+    if (contains(hash))
+        fFound = true;
+
+    for (unsigned int i = 0; i < tx.vout.size(); i++)
+    {
+        const CTxOut& txout = tx.vout[i];
+        // Match if the filter contains any arbitrary script data element in any scriptPubKey in tx
+        // If this matches, also add the specific output that was matched.
+        // This means clients don't have to update the filter themselves when a new relevant tx
+        // is discovered in order to find spending transactions, which avoids round-tripping and race conditions.
+        CScript::const_iterator pc = txout.scriptPubKey.begin();
+        std::vector<unsigned char> data;
+        while (pc < txout.scriptPubKey.end())
+        {
+            opcodetype opcode;
+            if (!txout.scriptPubKey.GetOp(pc, opcode, data))
+                break;
+            if (data.size() != 0 && contains(data))
+            {
+                fFound = true;
+                if ((nFlags & BLOOM_UPDATE_MASK) == BLOOM_UPDATE_ALL)
+                    insert(COutPoint(hash, i));
+                else if ((nFlags & BLOOM_UPDATE_MASK) == BLOOM_UPDATE_P2PUBKEY_ONLY)
+                {
+                    std::vector<std::vector<unsigned char> > vSolutions;
+                    TxoutType type = Solver(txout.scriptPubKey, vSolutions);
+                    if (type == TxoutType::PUBKEY || type == TxoutType::MULTISIG) {
+                        insert(COutPoint(hash, i));
+                    }
+                }
+                break;
+            }
+        }
+    }
+
+    if (fFound)
+        return true;
+
+    for (const CTxIn& txin : tx.vin)
+    {
+        // Match if the filter contains an outpoint tx spends
+        if (contains(txin.prevout))
+            return true;
+
+        // Match if the filter contains any arbitrary script data element in any scriptSig in tx
+        CScript::const_iterator pc = txin.scriptSig.begin();
+        std::vector<unsigned char> data;
+        while (pc < txin.scriptSig.end())
+        {
+            opcodetype opcode;
+            if (!txin.scriptSig.GetOp(pc, opcode, data))
+                break;
+            if (data.size() != 0 && contains(data))
+                return true;
+        }
+    }
+
+    return false;
+}
+
+CRollingBloomFilter::CRollingBloomFilter(const unsigned int nElements, const double fpRate)
+{
+    double logFpRate = log(fpRate);
+    /* The optimal number of hash functions is log(fpRate) / log(0.5), but
+     * restrict it to the range 1-50. */
+    nHashFuncs = std::max(1, std::min((int)round(logFpRate / log(0.5)), 50));
+    /* In this rolling bloom filter, we'll store between 2 and 3 generations of nElements / 2 entries. */
+    nEntriesPerGeneration = (nElements + 1) / 2;
+    uint32_t nMaxElements = nEntriesPerGeneration * 3;
+    /* The maximum fpRate = pow(1.0 - exp(-nHashFuncs * nMaxElements / nFilterBits), nHashFuncs)
+     * =>          pow(fpRate, 1.0 / nHashFuncs) = 1.0 - exp(-nHashFuncs * nMaxElements / nFilterBits)
+     * =>          1.0 - pow(fpRate, 1.0 / nHashFuncs) = exp(-nHashFuncs * nMaxElements / nFilterBits)
+     * =>          log(1.0 - pow(fpRate, 1.0 / nHashFuncs)) = -nHashFuncs * nMaxElements / nFilterBits
+     * =>          nFilterBits = -nHashFuncs * nMaxElements / log(1.0 - pow(fpRate, 1.0 / nHashFuncs))
+     * =>          nFilterBits = -nHashFuncs * nMaxElements / log(1.0 - exp(logFpRate / nHashFuncs))
+     */
+    uint32_t nFilterBits = (uint32_t)ceil(-1.0 * nHashFuncs * nMaxElements / log(1.0 - exp(logFpRate / nHashFuncs)));
+    data.clear();
+    /* For each data element we need to store 2 bits. If both bits are 0, the
+     * bit is treated as unset. If the bits are (01), (10), or (11), the bit is
+     * treated as set in generation 1, 2, or 3 respectively.
+     * These bits are stored in separate integers: position P corresponds to bit
+     * (P & 63) of the integers data[(P >> 6) * 2] and data[(P >> 6) * 2 + 1]. */
+    data.resize(((nFilterBits + 63) / 64) << 1);
+    reset();
+}
+
+/* Similar to CBloomFilter::Hash */
+static inline uint32_t RollingBloomHash(unsigned int nHashNum, uint32_t nTweak, Span<const unsigned char> vDataToHash)
+{
+    return MurmurHash3(nHashNum * 0xFBA4C795 + nTweak, vDataToHash);
+}
+
+
+// A replacement for x % n. This assumes that x and n are 32bit integers, and x is a uniformly random distributed 32bit value
+// which should be the case for a good hash.
+// See https://lemire.me/blog/2016/06/27/a-fast-alternative-to-the-modulo-reduction/
+static inline uint32_t FastMod(uint32_t x, size_t n) {
+    return ((uint64_t)x * (uint64_t)n) >> 32;
+}
+
+void CRollingBloomFilter::insert(Span<const unsigned char> vKey)
+{
+    if (nEntriesThisGeneration == nEntriesPerGeneration) {
+        nEntriesThisGeneration = 0;
+        nGeneration++;
+        if (nGeneration == 4) {
+            nGeneration = 1;
+        }
+        uint64_t nGenerationMask1 = 0 - (uint64_t)(nGeneration & 1);
+        uint64_t nGenerationMask2 = 0 - (uint64_t)(nGeneration >> 1);
+        /* Wipe old entries that used this generation number. */
+        for (uint32_t p = 0; p < data.size(); p += 2) {
+            uint64_t p1 = data[p], p2 = data[p + 1];
+            uint64_t mask = (p1 ^ nGenerationMask1) | (p2 ^ nGenerationMask2);
+            data[p] = p1 & mask;
+            data[p + 1] = p2 & mask;
+        }
+    }
+    nEntriesThisGeneration++;
+
+    for (int n = 0; n < nHashFuncs; n++) {
+        uint32_t h = RollingBloomHash(n, nTweak, vKey);
+        int bit = h & 0x3F;
+        /* FastMod works with the upper bits of h, so it is safe to ignore that the lower bits of h are already used for bit. */
+        uint32_t pos = FastMod(h, data.size());
+        /* The lowest bit of pos is ignored, and set to zero for the first bit, and to one for the second. */
+        data[pos & ~1] = (data[pos & ~1] & ~(((uint64_t)1) << bit)) | ((uint64_t)(nGeneration & 1)) << bit;
+        data[pos | 1] = (data[pos | 1] & ~(((uint64_t)1) << bit)) | ((uint64_t)(nGeneration >> 1)) << bit;
+    }
+}
+
+bool CRollingBloomFilter::contains(Span<const unsigned char> vKey) const
+{
+    for (int n = 0; n < nHashFuncs; n++) {
+        uint32_t h = RollingBloomHash(n, nTweak, vKey);
+        int bit = h & 0x3F;
+        uint32_t pos = FastMod(h, data.size());
+        /* If the relevant bit is not set in either data[pos & ~1] or data[pos | 1], the filter does not contain vKey */
+        if (!(((data[pos & ~1] | data[pos | 1]) >> bit) & 1)) {
+            return false;
+        }
+    }
+    return true;
+}
+
+void CRollingBloomFilter::reset()
+{
+    nTweak = GetRand(std::numeric_limits<unsigned int>::max());
+    nEntriesThisGeneration = 0;
+    nGeneration = 1;
+    std::fill(data.begin(), data.end(), 0);
+}
diff --git a/src/common/bloom.h b/src/common/bloom.h
new file mode 100644
index 0000000000..25c16fbfe2
--- /dev/null
+++ b/src/common/bloom.h
@@ -0,0 +1,127 @@
+// 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