* Summary CoinJoin, CoinSwap and similar technologies improve your privacy by making sure information about what coins you own doesn't make it into the blockchain, but syncing your wallet is a privacy risk in itself and can easily leak that same info. Here's an overview of that risk, how to quantify it, and how to reduce it efficiently.
* Background In the most general sense a Bitcoin wallet is a collection of one or more scriptPubKeys, often known as addresses.(*) The basic purpose of the wallet is maintain the set of all transaction outputs (txouts) matching the scriptPubKeys in the wallet. Secondary to that purpose is to maintain the set of all transactions associated with scriptPubKeys in the wallet; almost all (all?) wallet software maintains transaction information rather than only txout data. Usually, but not always, the wallet will have some mechanism to spend transaction outputs, creating new transactions. (if the wallet doesn't it is referred to as a watch-only wallet) Given a full set of blockchain data the task of keeping the set of all relevant transactions and txouts up-to-date is simple: scan the blockchain for the relevant data. The challenge is to devise systems where wallets can be kept up to date without this requirement in a way that is secure, efficient, scalable, and meets the user's privacy requirements. *) Alternatively addresses can be thought of as instructions to the payor as to how to generate a scriptPubKey that the payee can spend, a subtlety different concept. * Threat Model and Goals Currently the Bitcoin network consists of a large (low thousands) number of allegedly independent nodes. There is no mechanism to prevent an attacker from sybil attacking the network other than the availability of IP addresses. This protection is made even weaker by the difficulty of being sure you have a non-sybilled list of nodes to connect too; IP addresses are passed gossip-style with no authentication. From a privacy perspective we are conservative and assume an active, internal, and global attacker - using the terminology of Diaz et al.(1) - that controls up to 100% of the nodes you are connected too. With regard to retrieval of blockchain data we can use the Sweeney's notion of k-anonymity(2) where the privacy-sensitive data for an individual is obscured by it's inclusion in a data of a large set of individuals, the anonymity set. * Basic Functionality With regard to blockchain data we have the following basic functions: ** Spending funds The user creates a transaction and gets it to miners by some method, usually the P2P network although also possibly by direct submission. Either way privacy can be achieved through a mix network such as Tor and/or relaying other users' transactions so as to embed yours within a larger anonymity set. In some cases payment protocols can shift the problem to the recipient of the funds. Using CoinJoin also helps increase the anonymity set. Usually the sender will want to determine when the transaction confirms; once the transaction has confirmed modulo a reorganization the confirmation count can only increase. Transaction mutability and double-spends by malicious CoinJoin participants complicate the task of detecting confirmation: ideally we could simply query for the presence of a given txid in each new block, however the transaction could be mutated, changing the txid. The most simple way to detect confirmation is then to query for spends of the txouts spend by the transaction. ** Receiving new funds While in the future payment protocols may give recipients transaction information directly it is most likely that wallets will continue to have to query peers for new transactions paying scriptPubKey's under the user's control for the forseeable future. ** Detection of unauthorized spends Users' want early detection of private key compromise, accomplished by querying blockchain data for spends from txouts in their wallets. This has implications for how change must be handled, discussed below. * Scalability/Efficiency The total work done by the system as a whole for all queries given some number of transactions n is the scalability of the scheme. In addition scalability, and privacy in some cases, is improved if work can be easily spread out across multiple nodes both at a per-block and within-block level. * Reliability/Robustness Deterministic wallets using BIP32 or similar, where all private keys are derived from a fixed seed, have proven to be extremely popular with users for their simple backup model. While losing transaction metadata after a data-loss event is unfortunate, losing access to all funds is a disaster. Any address generation scheme must take this into account and make it possible for all funds to be recovered quickly and efficiently from blockchain data. Preserving privacy during this recovery is a consideration, but 100% recovery of funds should not be sacrificed for that goal. * Query schemes ** Bloom filters BIP37 bloom filters are currently implemented by the Bitcoin reference implementation and used by bitcoinj-based SPV clients. Bloom filters achieve a privacy-bandwidth tradeoff by having probabalistic false-positives; the false-positives comprise the anonymity set. Boom filters have a number of problems, both in the specific BIP37 implementation, as well as fundemental to the idea. Scalability is a serious problem: the client sends asks a peer with a copy of all blockchain data to filter data sent to the client, limiting the client's bandwidth to only the data they are interested in. In the typical case of a SPV wallet syncronizing against m new blocks this requires the peer to read those m blocks from disk in their entirety, apply the filter, and send the client the subset of matching transactions. Obviously this results in poor O(n^2) scaling for n clients each making some fixed number of transactions. Of course bloom filters are attractive in that they have very good performance per match, but this performance is only really relevant for the most recent blockchain information where the data is in RAM. For older information they make possible the Bloom IO attack where an attacker uses an inordinant amount of disk IO bandwidth at little cost to themselves.(3) The actual BIP37 standard, and existing implementations of it, have a number of other flaws that reduce privacy. For instance the standard lets the seed value of the hash function be tweaked with a 32-bit integer, nTweak. However on the one hand if randomly chosen and rarely changed, as suggested by BIP37, the 32-bit integer can be used by an attacker to correlate multiple connections from the same wallet. On the other hand if nTweak is changed an attacker that can link multiple bloom filters can AND those filters together to greatly decrease the false-positive rate and determine exactly what funds are in the user's wallet. ** Prefix filters With a randomly distributed keyspace - common in cryptographic applications - clients can query using variable length prefixes that partially match the desired keys. A very simple format for a query of n prefixes will look like the following: <1 byte length in bits> <1 to 256/8 bytes of prefix> ... ... 0x00 The anonymity set is then the blockchain data whose key is the same prefix, usually H(scriptPubKey) or scriptPubKey directly. An important advantage of prefix filters is compatibility with the proposed (U)TXO commitment schemes: the prefix maps directly to the committed scriptPubKey lookup trees, and nodes simply return all entries matching the prefix, as well the the rest of the merkle path to the blockchain headers proving the data is valid. While bloom filters have O(n) cost per lookup, or O(n^2) scalability system-wide, prefix filters have significantly better O(log n) cost per lookup, or O(n log n) system-wide. It's also worth noting that a naive implementation can achieve very similar performance to bloom filters without bothering to build key-value indexes by just scanning blockchain data; once the data is hashed testing the hash against a prefix has a minimal cost. ** Cryptographically blinded schemes There are many blinded database query schemes in existence. While we do not reject such schemes completely, technologies that rely on simple and easy-to-understand cryptography have a significant advantage in their simplicity. In addition such complex schemes are unlikely to ever be made into miner commitments and thus are less trustworthy in the long run. * Correlation attacks It is often advantageous if blockchain queries can be efficiently spread across multiple servers to avoid allowing the attacker to correllate the information into a whole. If you have n addresses that need to be watched for new transactions, splitting the queries across m nodes reduces the information any one node may learn. With bloom filters doing this is extremely costly as the full blockchain data needs to be read from disk to apply the filter; with prefix filters if the nodes have appropriate indexes there is little overhead to splitting the queries and no performance loss. * DoS attacks A possible DoS attack on bandwidth is to insert a large amount of blockchain data matching the target's filter; the BIP37 nTweak parameter was an attempt to avoid this problem, although with privacy tradeoffs. Blockchain data is an extremely expensive communications channel so we do not consider this a serious issue. Implementations may wish to give clients the ability to specify a filter for information they do not want to avoid unintentional collisions, although hopefully in the future the address reuse making this a potential problem will become less common. * Address use, management, and generation If privacy was not a consideration the most efficient mode of operation would be to use a single address, as is done by many existing wallets, notably the bitcoinj-derived Multibit and Android Wallet, both of which use bloom filters. In addition to strongly encouraging address re-use, neither provide the user any control over the privacy/bandwidth tradeoff given by bloom filters; the default settings have an extremely low false-positive rate that is a significant privacy risk. Taking privacy into account better clients such as Electrum, Armory, and Bitcoin Core discourage the re-use of addresses in their UIs, and send change to new addresses. However this leads to problem with user expectations: users expect it to be possible to be notified quickly of new transactions paying any address ever generated by their wallet, as well as unauthorized spends from any txout, yet for privacy each query for transactions related to the address/txout must match false-positives that consume bandwidth; for a fixed bandwidth budget the specificity and size of the filter must increase over time. We have two main avenues to solve this problem: 1) Txin-reuse: Continue to reinforce the idea that transaction inputs have no particular relationship to outputs. Using them for refunds or other purposes implying "ownership" must be strongly discouraged. CoinJoin will help here. If addresses associated with change txouts are truly one-time-use, we can reduce or eliminate queries associated with them. In particular, while the set of all change addresses ever used will grow linearly with time, the set of all change addresses with funds in them will remain roughly stable - it's this set that needs to be queried to detect unauthorized spends. 2) Common prefixes: Generate addresses such that for a given wallet they all share a fixed prefix. The length of that prefix determines the anonymity set and associated privacy/bandwidth tradeoff, which remainds a fixed ratio of all transactions for the life of the wallet. With this approach change addresses continue to be generated randomly, a requirement for CoinJoin privacy. There is some statistical information leaked if many non-change txouts are spent in a single transaction in a CoinJoin, but even that leak can be avoided with the authors OP_RETURN-based stealth addresses proposal. (to be published) The actual prefix-forcing scheme in many cases will have to be brute-force search; fortunately the search space involved is reasonably small, ~2 to ~16 bits, and can be done as a background task. 1) Towards Measuring Anonymity, Claudia Diaz and Stefaan Seys and Joris Claessens and Bart Preneel (April 2002) 2) k-Anonymity: A Model for Protecting Privacy, Latanya Sweeney, May 2002 3) Private discussions with developers. -- 'peter'[:-1]@petertodd.org 000000000000000f9102d27cfd61ea9e8bb324593593ca3ce6ba53153ff251b3
Description: Digital signature
------------------------------------------------------------------------------ Rapidly troubleshoot problems before they affect your business. Most IT organizations don't have a clear picture of how application performance affects their revenue. With AppDynamics, you get 100% visibility into your Java,.NET, & PHP application. Start your 15-day FREE TRIAL of AppDynamics Pro! http://pubads.g.doubleclick.net/gampad/clk?id=84349831&iu=/4140/ostg.clktrk
_______________________________________________ Bitcoin-development mailing list Bitcoinfirstname.lastname@example.org https://lists.sourceforge.net/lists/listinfo/bitcoin-development