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The Bitcoin scalability problem has led to the development of offchain financial mechanisms such as payment channel networks (PCNs) which help users process transactions of varying amounts, including micro-payment transactions, without writing each transaction to the blockchain. Since PCNs only allow path-based transactions, effective, secure routing protocols that find a path between a sender and receiver are fundamental to PCN operations. In this paper, we propose RACED, a routing protocol that leverages the idea of Distributed Hash Tables (DHTs) to route transactions in PCNs in a fast and secure way. Our experiments on real-world transaction datasets show that RACED gives an average transaction success ratio of 98.74%, an average pathfinding time of 31.242 seconds, which is 1.65 × 103, 1.8 × 103, and 4 × 102 times faster than three other recent routing protocols that offer comparable security/privacy properties. We rigorously analyze and prove the security of RACED in the Universal Composability framework. 

The Bitcoin blockchain scalability problem has inspired several offchain solutions for enabling cryptocurrency transactions, of which Layer-2 systems such as payment channel networks (PCNs) have emerged as a frontrunner. PCNs allow for path-based transactions between users without the need to access the blockchain. These path-based transactions are possible only if a suitable path exists from the sender of a payment to the receiver. In this paper, we propose Auroch, a distributed auction-based pathfinding and routing protocol that takes into account the routing fees charged by nodes along a path. Unlike other routing protocols proposed for PCNs, Auroch takes routing fees into consideration. Auroch maximizes the profit that can be achieved by an intermediate node at the same time minimizing the overall payment cost for the sender. 

Payment channel networks are a promising solution to the scalability challenge of blockchains and are designed for significantly increased transaction throughput compared to the layer one blockchain. Since payment channel networks are essentially decentralized peerto- peer networks, routing transactions is a fundamental challenge. Payment channel networks have some unique security and privacy requirements that make pathfinding challenging, for instance, network topology is not publicly known, and sender/receiver privacy should be preserved, in addition to providing atomicity guarantees for payments. In this paper, we present an efficient privacypreserving routing protocol, SPRITE, for payment channel networks that supports concurrent transactions. By finding paths offline and processing transactions online, SPRITE can process transactions in just two rounds, which is more efficient compared to prior work. We evaluate SPRITE’s performance using Lightning Network data and prove its security using the Universal Composability framework. In contrast to the current cutting-edge methods that achieve rapid transactions, our approach significantly reduces the message complexity of the system by 3 orders of magnitude while maintaining similar latencies. 

Pervasive Edge Computing (PEC), a recent addition to the edge computing paradigm, leverages the computing resources of end-user devices to execute computation tasks in close proximity to users. One of the primary challenges in the PEC environment is determining the appropriate servers for offloading computation tasks based on factors, such as computation latency, response quality, device reliability, and cost of service. Computation outsourcing in the PEC ecosystem requires additional security and privacy considerations. Finally, mechanisms need to be in place to guarantee fair payment for the executed service(s). We present 𝑃𝐸𝑃𝑃𝐸𝑅, a novel, privacy-preserving, and decentralized framework that addresses aforementioned challenges by utilizing blockchain technology and trusted execution environments (TEE). 𝑃𝐸𝑃𝑃𝐸𝑅 improves the performance of PEC by allocating resources among end-users efficiently and securely. It also provides the underpinnings for building a financial ecosystem at the pervasive edge. To evaluate the effectiveness of 𝑃𝐸𝑃𝑃𝐸𝑅, we developed and deployed a proof of concept implementation on the Ethereum blockchain, utilizing Intel SGX as the TEE technology. We propose a simple but highly effective remote attestation method that is particularly beneficial to PEC compared to the standard remote attestation method used today. Our extensive comparison experiment shows that 𝑃𝐸𝑃𝑃𝐸𝑅 is 1.23× to 2.15× faster than the current standard remote attestation procedure. In addition, we formally prove the security of our system using the universal composability (UC) framework. 

The growing popularity of Machine Learning (ML) has led to its deployment in various sensitive domains, which has resulted in significant research focused on ML security and privacy. However, in some applications, such as Augmented/Virtual Reality, integrity verification of the outsourced ML tasks is more critical–a face that has not received much attention. Existing solutions, such as multi-party computation and proof-based systems, impose significant computation overhead, which makes them unfit for real-time applications. We propose Fides, a novel framework for real-time integrity validation of ML-as-a-Service (MLaaS) inference. Fides features a novel and efficient distillation technique–Greedy Distillation Transfer Learning–that dynamically distills and fine-tunes a space and compute-efficient verification model for verifying the corresponding service model while running inside a trusted execution environment. Fides features a client-side attack detection model that uses statistical analysis and divergence measurements to identify, with a high likelihood, if the service model is under attack. Fides also offers a re-classification functionality that predicts the original class whenever an attack is identified. We devised a generative adversarial network framework for training the attack detection and re-classification models. The evaluation shows that Fides achieves an accuracy of up to 98% for attack detection and 94% for re-classification. 

In this paper, we present an efficient strategy to enumerate the number of k-cycles, g≤k<2g, in the Tanner graph of a quasi-cyclic low-density parity-check (QC-LDPC) code with girth g using its polynomial parity-check matrix H. This strategy works for both (dv,dc)-regular and irregular QC-LDPC codes. In this approach, we note that the mth power of the polynomial adjacency matrix can be used to describe walks of length m in the protograph and can therefore be sufficiently described by the matrices Bm(H)(HHT)m/2H(m2), where m≥0. We provide formulas for the number of k-cycles, Nk, by just taking into account repetitions in some multisets constructed from the matrices Bm(H). This approach is shown to have low complexity. For example, in the case of QC-LDPC codes based on the 3×nv fully-connected protograph, the complexity of determining Nk, for k=4,6,8,10 and 12, is O(nv2log(N)), O(nv2log(nv)log(N)), O(nv4log4(nv)log(N)), O(nv4log(nv)log(N)) and O(nv6log6(nv)log(N)), respectively. The complexity, depending logarithmically on the lifting factor N, gives our approach, to the best of our knowledge, a significant advantage over previous works on the cycle distribution of QC-LDPC codes. 

In this paper, we investigate the problem of decoder error propagation for spatially coupled low-density parity-check (SC-LDPC) codes with sliding window decoding (SWD). This problem typically manifests itself at signal-to-noise ratios (SNRs) close to capacity under low-latency operating conditions. In this case, infrequent but severe decoder error propagation can sometimes occur. To help understand the error propagation problem in SWD of SC-LDPC codes, a multi-state Markov model is developed to describe decoder behavior and to analyze the error performance of spatially coupled LDPC codes under these conditions. We then present two approaches -check node (CN) doping and variable node (VN) doping -to combating decoder error propagation and improving decoder performance. Next we describe how the performance can be further improved by employing an adaptive approach that depends on the availability of a noiseless binary feedback channel. To illustrate the effectiveness of the doping techniques, we analyze the error performance of CN doping and VN doping using the multi-state decoder model. We then present computer simulation results showing that CN and VN doping significantly improve the performance in the operating range of interest at a cost of a small rate loss and that adaptive doping further improves the performance. We also show that the rate loss is always less than that resulting from encoder termination and can be further reduced by doping only a fraction of the VNs at each doping position in the code graph with only a minor impact on performance. Finally, we show how the encoding problem for VN doping can be greatly simplified by doping only systematic bits, with little or no performance loss.