1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
|
// Copyright (c) 2012-2022 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/solver.h>
#include <span.h>
#include <streams.h>
#include <util/fastrange.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)
{
DataStream stream{};
stream << outpoint;
insert(MakeUCharSpan(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
{
DataStream stream{};
stream << outpoint;
return contains(MakeUCharSpan(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);
}
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 = FastRange32(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 & ~1U] = (data[pos & ~1U] & ~(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 = FastRange32(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 & ~1U] | data[pos | 1]) >> bit) & 1)) {
return false;
}
}
return true;
}
void CRollingBloomFilter::reset()
{
nTweak = GetRand<unsigned int>();
nEntriesThisGeneration = 0;
nGeneration = 1;
std::fill(data.begin(), data.end(), 0);
}
|