Evicting and filling attack for linking multiple network addresses of Bitcoin nodes

Bitcoin is a decentralized P2P cryptocurrency.It supports users to use pseudonyms instead of network addresses to send and receive transactions at the data layer,hiding users'real network identities.Traditional transaction tracing attack cuts through the network layer to directly associate each transaction with the network address that issued it,thus revealing the sender's network identity.But this attack can be mitigated by Bitcoin's network layer privacy protections.Since Bitcoin protects the unlinkability of Bitcoin addresses and there may be a many-to-one relation-ship between addresses and nodes,transactions sent from the same node via different addresses are seen as com-ing from different nodes because attackers can only use addresses as node identifiers.In this paper,we proposed the evicting and flling attack to expose the correlations between addresses and cluster transactions sent from different addresses of the same node.The attack exploited the unisolation of Bitcoin's incoming connection processing mecha-nism.In particular,an attacker can utilize the shared connection pool and deterministic connection eviction strategy to infer the correlation between incoming and evicting connections,as well as the correlation between releasing and flling connections.Based on inferred results,different addresses of the same node with these connections can be linked together,whether they are of the same or different network types.We designed a multi-step attack procedure,and set reasonable attack parameters through analyzing the factors that affect the attack effciency and accuracy.We mounted this attack on both our self-run nodes and multi-address nodes in real Bitcoin network,achieving an aver-age accuracy of 96.9% and 82%,respectively.Furthermore,we found that the attack is also applicable to Zcash,Litecoin,Dogecoin,Bitcoin Cash,and Dash.We analyzed the cost of network-wide attacks,the application scenario,and proposed countermeasures of this attack.

An efficient confidentiality protection solution for pub/sub system

Publish/subscribe(pub/sub)systems are widely used in large-scale messaging systems due to their asynchronous and decoupled nature.With the population of pub/sub cloud services,the privacy protection problem of pub/sub systems has started to emerge,and events and subscriptions are exposed when executing event matching on untrustworthy cloud brokers.However,as the number of subscriptions increases,the effectiveness of the previous confidentiality protection approaches declines drastically.In this paper,we propose SBM(scalable blind matching),an effective confidentiality protection scheme for pub/sub systems.To the best of our knowledge,SBM is the first scheme that applies order-preserving encryption algorithm to protect the system’s confidentiality and ensure its scalability.In this scheme,SBM-I is highly effective in subscription matching but is unable to achieve ideal security IND-OCPA,whereas SBM-II is suggested to ensure system security and SGX is used to reduce interaction and boost ciphertext matching performance.The experiment demonstrates that this method has better matching performance compared to others:the average matching time of SBM-I is 3–4 orders of magnitude faster than the matching algorithm MP and SGX-based algorithm SCBR when the number of subscriptions is 500,000,and the average matching time of SBM-II is 40 times faster than MP and 24 times than SCBR.