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For distributed protocols involving many servers, assuming that they do not collude with each other makes some secrecy problems solvable and reduces overheads and computational hardness assumptions in others. While the non-collusion assumption is pervasive among privacy-preserving systems, it remains highly susceptible to covert, undetectable collusion among computing parties. This work stems from an observation that if the number of available computing parties is much higher than the number of parties required to perform a secure computation, collusion attempts could be deterred. We focus on the standard problem of multi-server private information retrieval (PIR) that inherently assumes that servers do not collude. For PIR application scenarios, such as those for blockchain light clients, where the available servers are plentiful, a single server's deviating action is not tremendously beneficial to itself. We can make deviations undesired through small amounts of rewards and penalties, thus raising the bar for collusion significantly. For any given multi-server 1-private PIR (i.e. the base PIR scheme is constructed assuming no pairwise collusion), we provide a collusion mitigation mechanism. We first define a two-stage sequential game that captures how rational servers interact with each other during collusion, then determine the payment rules such that the game realizes the unique sequential equilibrium: a non-collusion outcome. We also offer privacy protection for an extended period beyond the time the query executions happen, and guarantee user compensation in case of a reported privacy breach. Overall, we conjecture that the incentive structure for collusion mitigation to be functional towards relaxing the strong non-collusion assumptions across a variety of multi-party computation tasks.

Brain computing interfaces (BCI) are used in a plethora of safety/privacy-critical applications, ranging from healthcare to smart communication and control. Wearable BCI setups typically involve a head-mounted sensor connected to a mobile device, combined with ML-based data processing. Consequently, they are susceptible to a multiplicity of attacks across the hardware, software, and networking stacks used that can leak users' brainwave data or at worst relinquish control of BCI-assisted devices to remote attackers. In this paper, we: (i) analyse the whole-system security and privacy threats to existing wearable BCI products from an operating system and adversarial machine learning perspective; and (ii) introduce Argus, the first information flow control system for wearable BCI applications that mitigates these attacks. Argus' domain-specific design leads to a lightweight implementation on Linux ARM platforms suitable for existing BCI use-cases. Our proof of concept attacks on real-world BCI devices (Muse, NeuroSky, and OpenBCI) led us to discover more than 300 vulnerabilities across the stacks of six major attack vectors. Our evaluation shows Argus is highly effective in tracking sensitive dataflows and restricting these attacks with an acceptable memory and performance overhead (<15%).

We introduce Zef, the first Byzantine-Fault Tolerant (BFT) protocol to support payments in anonymous digital coins at arbitrary scale. Zef follows the communication and security model of FastPay: both protocols are asynchronous, low-latency, linearly-scalable, and powered by partially-trusted sharded authorities. In contrast with FastPay, user accounts in Zef are uniquely-identified and safely removable. Zef coins are bound to an account by a digital certificate and otherwise stored off-chain by their owners. To create and redeem coins, users interact with the protocol via privacy-preserving operations: Zef uses randomized commitments and NIZK proofs to hide coin values; and, created coins are made unlinkable using the blind and randomizable threshold anonymous credentials of Coconut. Besides the detailed specifications and our analysis of the protocol, we are making available an open-source implementation of Zef in Rust. Our extensive benchmarks on AWS confirm textbook linear scalability and demonstrate a confirmation time under one second at nominal capacity. Compared to existing anonymous payment systems based on a blockchain, this represents a latency speedup of three orders of magnitude, with no theoretical limit on throughput.

The provision of communication services via portable and mobile devices, such as aerial base stations, is a crucial concept to be realized in 5G/6G networks. Conventionally, IoT/edge devices need to transmit the data directly to the base station for training the model using machine learning techniques. The data transmission introduces privacy issues that might lead to security concerns and monetary losses. Recently, Federated learning was proposed to partially solve privacy issues via model-sharing with base station. However, the centralized nature of federated learning only allow the devices within the vicinity of base stations to share the trained models. Furthermore, the long-range communication compels the devices to increase transmission power, which raises the energy efficiency concerns. In this work, we propose distributed federated learning (DBFL) framework that overcomes the connectivity and energy efficiency issues for distant devices. The DBFL framework is compatible with mobile edge computing architecture that connects the devices in a distributed manner using clustering protocols. Experimental results show that the framework increases the classification performance by 7.4\% in comparison to conventional federated learning while reducing the energy consumption.

Integrated sensing and communication (ISAC) has been regarded as one of the most promising technologies for future wireless communications. However, the mutual interference in the communication radar coexistence system cannot be ignored. Inspired by the studies of reconfigurable intelligent surface (RIS), we propose a double-RIS-assisted coexistence system where two RISs are deployed for enhancing communication signals and suppressing mutual interference. We aim to jointly optimize the beamforming of RISs and radar to maximize communication performance while maintaining radar detection performance. The investigated problem is challenging, and thus we transform it into an equivalent but more tractable form by introducing auxiliary variables. Then, we propose a penalty dual decomposition (PDD)-based algorithm to solve the resultant problem. Moreover, we consider two special cases: the large radar transmit power scenario and the low radar transmit power scenario. For the former, we prove that the beamforming design is only determined by the communication channel and the corresponding optimal joint beamforming strategy can be obtained in closed-form. For the latter, we minimize the mutual interference via the block coordinate descent (BCD) method. By combining the solutions of these two cases, a low-complexity algorithm is also developed. Finally, simulation results show that both the PDD-based and low-complexity algorithms outperform benchmark algorithms.

With the advances in 5G and IoT devices, the industries are vastly adopting artificial intelligence (AI) techniques for improving classification and prediction-based services. However, the use of AI also raises concerns regarding privacy and security that can be misused or leaked. Private AI was recently coined to address the data security issue by combining AI with encryption techniques, but existing studies have shown that model inversion attacks can be used to reverse engineer the images from model parameters. In this regard, we propose a Federated Learning and Encryption-based Private (FLEP) AI framework that provides two-tier security for data and model parameters in an IIoT environment. We proposed a three-layer encryption method for data security and provide a hypothetical method to secure the model parameters. Experimental results show that the proposed method achieves better encryption quality at the expense of slightly increased execution time. We also highlight several open issues and challenges regarding the FLEP AI framework's realization.

As the scale of distributed training grows, communication becomes a bottleneck. To accelerate the communication, recent works introduce In-Network Aggregation (INA), which moves the gradients summation into network middle-boxes, e.g., programmable switches to reduce the traffic volume. However, switch memory is scarce compared to the volume of gradients transmitted in distributed training. Although literature applies methods like pool-based streaming or dynamic sharing to tackle the mismatch, switch memory is still a potential performance bottleneck. Furthermore, we observe the under-utilization of switch memory due to the synchronization requirement for aggregator deallocation in recent works. To improve the switch memory utilization, we propose ESA, an $\underline{E}$fficient Switch Memory $\underline{S}$cheduler for In-Network $\underline{A}$ggregation. At its cores, ESA enforces the preemptive aggregator allocation primitive and introduces priority scheduling at the data-plane, which improves the switch memory utilization and average job completion time (JCT). Experiments show that ESA can improve the average JCT by up to $1.35\times$.

The cellular network standard is gradually stepping towards the 6th Generation (6G). In 6G, the pioneering and exclusive features, such as creating connectivity even in space and under water, are attracting Governments, organizations and researchers to spend time, money, effort extensively in this area. In the direction of intelligent network management and distributed secured systems, Artificial Intelligence (AI) and blockchain are going to form the backbone of 6G, respectively. However, there is a need for the study of the 6g architecture and technology, such that researchers can identify the scopes of improvement in 6G. Therefore, in this survey, we discuss the primary requirements of 6G along with its overall architecture and technological aspects. We also highlight crucial challenges and future research directions in 6G networks, which can lead to the successful practical implementation of 6G, as per the objective of its introduction in next generation cellular networks.

Classical one-sided matching assumes participants in the matching market are of a fixed size, each with an initial endowment and can exchange with others. In this paper, we consider a more dynamic and challenging setting where only a few participants are initially in the market, while the others need their invitation/permission to join in. However, the invitation does not always occur naturally and thus requires incentives. If we simply apply Top Trading Cycle, a classic solution for traditional matching, invitees may compete with their inviters in the matching and therefore they are reluctant to invite others. To combat this, we propose a new solution to protect inviters which guarantees that inviting all their friends is a dominant strategy for all participants. This solution novelly utilizes participants' invitations, which is not a simple extension of any existing solutions. We demonstrate its advantages in terms of participants' satisfaction by simulations and compare it with other existing solutions.

Graph Convolutional Networks (GCNs) have proved to be a most powerful architecture in aggregating local neighborhood information for individual graph nodes. Low-rank proximities and node features are successfully leveraged in existing GCNs, however, attributes that graph links may carry are commonly ignored, as almost all of these models simplify graph links into binary or scalar values describing node connectedness. In our paper instead, links are reverted to hypostatic relationships between entities with descriptional attributes. We propose GCN-LASE (GCN with Link Attributes and Sampling Estimation), a novel GCN model taking both node and link attributes as inputs. To adequately captures the interactions between link and node attributes, their tensor product is used as neighbor features, based on which we define several graph kernels and further develop according architectures for LASE. Besides, to accelerate the training process, the sum of features in entire neighborhoods are estimated through Monte Carlo method, with novel sampling strategies designed for LASE to minimize the estimation variance. Our experiments show that LASE outperforms strong baselines over various graph datasets, and further experiments corroborate the informativeness of link attributes and our model's ability of adequately leveraging them.