Automated market makers (AMMs) are a prime example of Web 3.0 applications. Their popularity and high trading activity led to serious scalability issues in terms of throughput and state size. In this paper, we address these challenges by utilizing a new sidechain architecture, building a system called ammBoost. ammBoost reduces the amount of on-chain transactions, boosts throughput, and supports blockchain pruning. We devise several techniques to enable layer 2 processing for AMMs, including a functionality-split and layer 2 traffic summarization paradigm, an epoch-based deposit mechanism, and pool snapshot-based and delayed token-payout trading. We also build a proof-of-concept for a Uniswap-inspired use case to empirically evaluate performance. Our experiments show that ammBoost decreases the gas cost by 96.05% and the chain growth by at least 93.42%, and that it can support up to 500x of the daily traffic volume of Uniswap.

Federated learning enables users to collaboratively train a machine learning model over their private datasets. Secure aggregation protocols are employed to mitigate information leakage about the local datasets from user-submitted model updates. This setup, however, still leaks the user participation in training, which can also be sensitive. Protecting user anonymity is even more challenging in dynamic environments where users may (re)join or leave the training process at any point of time. This paper introduces AnoFel, the first framework to support private and anonymous dynamic participation in federated learning (FL). AnoFel leverages several cryptographic primitives, the concept of anonymity sets, differential privacy, and a public bulletin board to support anonymous user registration, as well as unlinkable and confidential model update submission. Our system allows dynamic participation, where users can join or leave at any time without needing any recovery protocol or interaction. To assess security, we formalize a notion for privacy and anonymity in FL, and formally prove that AnoFel satisfies this notion. To the best of our knowledge, our system is the first solution with provable anonymity guarantees. To assess efficiency, we provide a concrete implementation of AnoFel, and conduct experiments showing its ability to support learning applications scaling to a large number of clients. For a TinyImageNet classification task with 512 clients, the client setup to join is less than 3 sec, and the client runtime for each training iteration takes a total of 8 sec, where the added overhead of AnoFel is 46% of the total runtime. We also compare our system with prior work and demonstrate its practicality. AnoFel client runtime is up to 5x faster than Truex et al., despite the added anonymity guarantee and dynamic user joining in AnoFel. Compared to Bonawitz et al., AnoFel is only 2x slower for added support for privacy in output, dynamic user joining, and anonymity.

Layer-two blockchain protocols emerged to address scalability issues related to fees, storage cost, and confirmation delay of on-chain transactions. They aggregate off-chain transactions into fewer on-chain ones, thus offering immediate settlement and reduced transaction fees. To preserve security of the underlying ledger, layer-two protocols often work in a collateralized model; resources are committed on-chain to backup off-chain activities. A fundamental challenge that arises in this setup is determining a policy for establishing, committing, and replenishing the collateral in a way that maximizes the value of settled transactions. In this paper, we study this problem under two settings that model collateralized layer-two protocols. The first is a general model in which a party has an on-chain collateral C with a policy to decide on whether to settle or discard each incoming transaction. The policy also specifies when to replenish C based on the remaining collateral value. The second model considers a discrete setup in which C is divided among k wallets, each of which is of size C/k, such that when a wallet is full, and so cannot settle any incoming transactions, it will be replenished. We devise several online policies for these models, and show how competitive they are compared to optimal (offline) policies that have full knowledge of the incoming transaction stream. To the best of our knowledge, we are the first to study and formulate online competitive policies for collateral and wallet management in the blockchain setting.

Cryptocurrencies and blockchain technology provide an innovative model for reshaping digital services. Driven by the movement toward Web 3.0, recent systems started to provide distributed services, such as computation outsourcing or file storage, on top of the currency exchange medium. By allowing anyone to join and collect cryptocurrency payments for serving others, these systems create decentralized markets for trading digital resources. Yet, there is still a big gap between the promise of these markets and their practical viability. Existing initiatives are still early-stage and have already encountered security and efficiency obstacles. At the same time, existing work around promising ideas, specifically sidechains, fall short in exploiting their full potential in addressing these problems. To bridge this gap, we propose chainBoost, a secure performance booster for decentralized resource markets. It expedites service related operations, reduces the blockchain size, and supports flexible service-payment exchange modalities at low overhead. At its core, chainBoost employs a sidechain, that has a (security and semantic) mutual-dependence with the mainchain, to which the system offloads heavy/frequent operations. To enable it, we develop a novel sidechain architecture composed of temporary and permanent blocks, a block suppression mechanism to prune the sidechain, a syncing protocol to permit arbitrary data exchange between the two chains, and an autorecovery protocol to support robustness and resilience. We analyze the security of chainBoost, and implement a proof-of-concept prototype for a distributed file storage market as a use case. For a market handling around 2000 transactions per round, our experiments show up to 11x improvement in throughput and 94% reduction in confirmation time. They also show that chainBoost can reduce the main blockchain size by ~90%, and that it outperforms comparable optimistic rollup solutions by reducing transaction finality by 99.7%.

Despite the great potential and flexibility of smart contract-enabled blockchains, building privacy-preserving applications using these platforms remains an open question. Existing solutions fall short since they ask end users to coordinate and perform the computation off-chain themselves. While such an approach reduces the burden of the miners of the system, it largely limits the ability of lightweight users to enjoy privacy since performing the actual computation on their own and attesting to its correctness is expensive even with state-of-the-art proof systems.To address this limitation, we propose smartFHE, a framework to support private smart contracts using fully homomorphic encryption (FHE). To the best of our knowledge, smartFHE is the first to use FHE in the blockchain model; moreover, it is the first to support arbitrary privacy-preserving applications for lightweight users under the same computation-on-demand model pioneered by Ethereum. smartFHE does not overload the user since miners are instead responsible for performing the private computation. This is achieved by employing FHE so miners can compute over encrypted data and account balances. Users are only responsible for proving well-formedness of their private inputs using efficient zero-knowledge proof systems (ZKPs). We formulate a notion for a privacy-preserving smart contract (PPSC) scheme and show a concrete instantiation of our smartFHE framework. We address challenges resulting from using FHE in the blockchain setting—including concurrency and dealing with leveled schemes. We also show how to choose suitable FHE and ZKP schemes to instantiate our framework, since naively choosing these will lead to poor performance in practice. We formally prove correctness and security of our construction. Finally, we conduct experiments to evaluate its efficiency, including comparisons with a state-of-the-art scheme and testing several private smart contract applications. We have open-sourced our (highly optimized) ZKP library, which could be of independent interest.

Privacy is a huge concern for cryptocurrencies and blockchains as most of these systems log everything in the clear. This has resulted in several academic and industrial initiatives to address privacy. Starting with the UTXO model of Bitcoin, initial works brought confidentiality and anonymity to payments. Recent works have expanded to support more generalized forms of private computation. Such solutions tend to be highly involved as they rely on advanced cryptographic primitives and creative techniques to handle issues related to dealing with private records (e.g. concurrency and double spending). This situation makes it hard to comprehend the current state-of-the-art, much less build on top of it. To address these challenges, we develop a systematization of knowledge for privacy-preserving solutions in blockchain. To the best of our knowledge, our work is the first of its kind. After motivating design challenges, we devise two systematization frameworks-the first as a stepping stone to the second- and use them to study the state-of-the-art. For our first framework, we study the zero-knowledge proof systems used in surveyed solutions, based on their key features and limitations. Our second is for privacy-preserving solutions; we define several dimensions to categorize the surveyed schemes and, in doing so, identify two major paradigms employed to achieve private computation. We go on to provide insights to guide solutions' adoption and development. Finally, we touch upon challenges related to limited functionality and accommodating new developments.

Existing models for non-interactive MPC cannot provide full privacy for inputs, because they inherently leak the residual function (i.e., the output of the function on the honest parties’ input together with all possible values of the adversarial inputs). For example, in any non-interactive sealed-bid auction, the last bidder can figure out what was the highest previous bid. We present a new MPC model which avoids this privacy leak. To achieve this, we utilize a blockchain in a novel way, incorporating smart contracts and arbitrary parties that can be incentivized to perform computation (“bounty hunters,” akin to miners). Security is maintained under a monetary assumption about the parties: an honest party can temporarily supply a recoverable collateral of value higher than the computational cost an adversary can expend. We thus construct non-interactive MPC protocols with strong security guarantees (full security, no residual leakage) in the short term. Over time, as the adversary can invest more and more computational resources, the security guarantee decays. Thus, our model, which we call Gage MPC, is suitable for secure computation with limited-time secrecy, such as auctions. A key ingredient in our protocols is a primitive we call “Gage Time Capsules” (GaTC): a time capsule that allows a party to commit to a value that others are able to reveal but only at a designated computational cost. A GaTC allows a party to commit to a value together with a monetary collateral. If the original party properly opens the GaTC, it can recover the collateral. Otherwise, the collateral is used to incentivize bounty hunters to open the GaTC. This primitive is used to ensure completion of Gage MPC protocols on the desired inputs. As a requisite tool (of independent interest), we present a generalization of garbled circuit that are more robust: they can tolerate exposure of extra input labels. This is in contrast to Yao’s garbled circuits, whose secrecy breaks down if even a single extra label is exposed. Finally, we present a proof-of-concept implementation of a special case of our construction, yielding an auction functionality over an Ethereum-like blockchain.

Recent compilers allow a general-purpose program (written in a conventional programming language) that handles private data to be translated into a secure distributed implementation of the corresponding functionality. The resulting program is then guaranteed to provably protect private data using secure multi-party computation techniques. The goals of such compilers are generality, usability, and efficiency, but the complete set of features of a modern programming language has not been supported to date by the existing compilers. In particular, recent compilers PICCO and the two-party ANSI C compiler strive to translate any C program into its secure multi-party implementation, but they currently lack support for pointers and dynamic memory allocation, which are important components of many C programs. In this work, we mitigate the limitation and add support for pointers to private data and consequently dynamic memory allocation to the PICCO compiler, enabling it to handle a more diverse set of programs over private data. Because doing so opens up a new design space, we investigate the use of pointers to private data (with known as well as private locations stored in them) in programs and report our findings. Aside from dynamic memory allocation, we examine other important topics associated with common pointer use such as reference by pointer/address, casting, and building various data structures in the context of secure multi-party computation. This results in enabling the compiler to automatically translate a user program that uses pointers to private data into its distributed implementation that provably protects private data throughout the computation. We empirically evaluate the constructions and report on the performance of representative programs.

Distance health monitoring is becoming an attractive option for both the patients and the medical sector staff. However, these systems face many design challenges including the large amount of processed data, users' mobility, and network coverage area. Accordingly, integrating cloud computing with Wireless Body Area Networks (WBANs) is amongst the best solutions to promote health monitoring systems. In this paper, we propose a cloud-based real-time remote health monitoring system. Several novel techniques are developed to optimize the performance of the proposed system. First, we utilize the concept of data classification and aggregation to reduce the traffic flow in the cloud. Second, a dynamic channel assignment policy is developed to distribute the users' WBANs among the available frequency channels to manage interference. Third, to optimize the delay of the delivered data a delay-aware routing metric is proposed to be used in the multi-hop routing. In addition, this metric is further utilized by the association protocols used by the WBANs to connect with the cloud. The simulation results prove the efficiency of the proposed architecture in optimizing the end-to-end delay, managing interference, maximizing the network capacity, and tracking the mobility of the users.

ABSTRACT In wireless sensor networks (WSNs), sensor devices have limited supply of energy. The sensor death due to dissipating battery energy is one of the fundamental design issues in WSNs. Hence, energy efficiency is argued to be the most important requirement for any protocol designed for WSNs. With sensors acting as routers to transport the packets from a source to a destination sensor, multipath protocols are used to discover multiple paths with the objective to improve the reliability, efficiency, and security in WSNs. Selecting the path that minimizes the rate of sensors death and extends the lifetime of the network is the main challenge for multipath protocols. In this paper, we propose a new energy and security aware route selection (ESARS) protocol for WSNs. The first part of ESARS selects a route that maximizes the network lifetime based on a novel metric. The second part of ESARS finds the optimal security level for the selected path based on the estimated security risk of the path. Traditionally, these two parts are addressed separately in the literature, and this paper combines the two parts in one protocol. The proposed protocol is evaluated and compared with other protocols using both analytical analysis and extensive simulations. The results show that the proposed protocol not only achieves its main objective to extend the network lifetime by significantly reducing the sensors death rate but also uses the most optimal security level for the selected route. Moreover, in ESARS protocol, several threshold parameters were employed to provide flexibility per the needs of the application in which the sensors are used. Copyright © 2013 John Wiley & Sons, Ltd.

Since enrgy is a vlaubale resource in Wireless Sensor Networks (WSNs), it is important to continuously monitor the status of this valuable network resource after network deployment. The information about energy status can be used to early notify both sensors and deployers of resource depletion in some parts of the network. It can also be used to perform energy-efficient routing in WSNs. In this paper, we propose a scheme for monitoring residual energy distributions at different parts of the network through a mechanism called Energy Centric scale (ECscale). ECscale is then used to perform optimal as well as approximate Energy-Centric Routing (ECR) in WSNs with the objective of maximizing the network lifetime. The proposed algorithms make use of a fixed virtual wireless backbone that is built on top of the physical topology. Simulation results show that our scheme is scalable and can provide many folds of energy savings when compared to conventional routing schemes.