Blockchain protocols are inherently limited in transaction throughput and latency. Recent efforts to address performance and scale blockchains have focused on off-chain payment channels. While such channels can achieve low latency and high throughput, deploying them securely on top of the Bitcoin blockchain has been difficult, partly because building a secure implementation requires changes to the underlying protocol and the ecosystem. We present Teechan, a full-duplex payment channel framework that exploits trusted execution environments. Teechan can be deployed securely on the existing Bitcoin blockchain without having to modify the protocol. It: (i) achieves a higher transaction throughput and lower transaction latency than prior solutions; (ii) enables unlimited full-duplex payments as long as the balance does not exceed the channel's credit; (iii) requires only a single message to be sent per payment in any direction; and (iv) places at most two transactions on the blockchain under any execution scenario. We have built and deployed the Teechan framework using Intel SGX on the Bitcoin network. Our experiments show that, not counting network latencies, Teechan can achieve 2,480 transactions per second on a single channel, with sub-millisecond latencies.
Direct payments were proposed by Chaum @cite_33 in ecash to achieve privacy. The ecash assumptions are significantly weaker than those offered by payment channels. Mainly, cheating is enforced in retrospect, through external punishment mechanisms.
@cite_33
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In Journal of Cryptology 1 1 (1988) 65–75, CHAUM describes a beautiful technique, the DC-net, which should allow participants to send and receive messages anonymously in an arbitrary network. The untraceability of the senders is proved to be unconditional, but that of the recipients implicitly assumes a reliable broadcast network. This assumption is unrealistic in some networks, but it can be removed completely by using the fail-stop key generation schemes by WAIDNER (these proceedings). In both cases, however, each participant can untraceably and permanently disrupt the entire DC-net. Payment channels emerged recently as an efficient method for performing cheap micropayments in cryptocurrencies. In contrast to traditional on-chain transactions, payment channels have the advantage that they allow for nearly unlimited number of transactions between parties without involving the blockchain. In this work, we introduce Perun, an off-chain channel system that offers a new method for connecting channels that is more efficient than the existing technique of routing transactions'' over multiple channels. To this end, Perun introduces a technique called virtual paymentchannels'' that avoids involvement of the intermediary for each individual payment.In this paper we formally model and prove security of this technique in the case of one intermediary, who can be viewed as a payment hub'' that has direct channels with several parties. Our scheme works over any cryptocurrency that provides Turing-complete smart contracts. As a proof of concept, we implemented Perun's smart contracts in Ethereum. In this paper, we consider the problem of estimating a potentially sensitive (individually stigmatizing) statistic on a population. In our model, individuals are concerned about their privacy, and experience some cost as a function of their privacy loss. Nevertheless, they would be willing to participate in the survey if they were compensated for their privacy cost. These cost functions are not publicly known, however, nor do we make Bayesian assumptions about their form or distribution. Individuals are rational and will misreport their costs for privacy if doing so is in their best interest. Ghosh and Roth recently showed in this setting, when costs for privacy loss may be correlated with private types, if individuals value differential privacy, no individually rational direct revelation mechanism can compute any non-trivial estimate of the population statistic. In this paper, we circumvent this impossibility result by proposing a modified notion of how individuals experience cost as a function of their privacy loss, and by giving a mechanism which does not operate by direct revelation. Instead, our mechanism has the ability to randomly approach individuals from a population and offer them a take-it-or-leave-it offer. This is intended to model the abilities of a surveyor who may stand on a street corner and approach passers-by. Oblivious transfer is a fundamental primitive in cryptography. While perfect information theoretic security is impossible, quantum oblivious transfer protocols can limit the dishonest player's cheating. Finding the optimal security parameters in such protocols is an important open question. In this paper we show that every 1-out-of-2 oblivious transfer protocol allows a dishonest party to cheat with probability bounded below by a constant strictly larger than 1 2. Alice's cheating is defined as her probability of guessing Bob's index, and Bob's cheating is defined as his probability of guessing both input bits of Alice. In our proof, we relate these cheating probabilities to the cheating probabilities of a bit commitment protocol and conclude by using lower bounds on quantum bit commitment. Then, we present an oblivious transfer protocol with two messages and cheating probabilities at most 3 4. Last, we extend Kitaev's semidefinite programming formulation to more general primitives, where the security is against a dishonest player trying to force the outcome of the other player, and prove optimal lower and upper bounds for them.