Computer Science (CS) education continues to face challenges in supporting neurodivergent learners, particularly when their cognitive characteristics do not align with traditional teaching practices. Inclusive frameworks such as Universal Design for Learning (UDL) and Culturally Relevant Pedagogy (CRP) offer useful principles but provide limited guidance on how to prioritise interventions when resources are constrained. This paper presents a trait-based prioritisation framework derived from a systematic analysis of 48 empirical findings across ten studies. From recurring patterns, we identify five cognitive dimensions—pattern recognition, attention regulation, detail-abstraction preference, sensory processing, and structure-related needs—and construct six learner profiles. Interventions are then organised into four tiers based on their impact, feasibility, and inclusivity. Tier 1 interventions address 61% of learner traits and resolve 83% of documented pedagogical misalignments while also benefiting neurotypical students. The framework offers an evidence-informed approach to linking cognitive patterns with instructional strategies in CS education and establishes a foundation for empirical validation.

A decentralized storage network (DSN) collects idle storage resources from Internet nodes for low-cost rental to users, and its scale has grown exponentially. In a DSN, most users rely on a centralized third-party service provider (SP) to process userside data and interact with decentralized storage nodes (SNs), making users suffer from a single point of failure. Additionally, since both SP and SNs may offer malicious services, enabling fault tolerance, data confidentiality, and availability guarantee with public verifiability is crucial in the presence of such threats. In this paper, we propose TolerStore, a completely decentralized service framework for DSNs with decentralized SPs and SNs, which can tolerate Byzantine SPs and malicious SNs. To the best of our knowledge, TolerStore is the first to develop a blockchain with multiple SPs for privacy-aware data processing in DSNs, which is formally proven to ensure Byzantine fault tolerance, data confidentiality, public verification, and data availability. Furthermore, we propose an optimized Byzantine Fault-Tolerant consensus with an adaptive leader rotation, incorporating homomorphic fingerprints to verify privacy-aware data processing with enhanced performance. We implement a TolerStore prototype over Hyperledger Fabric, and extensive experiments show that it tolerates [$\frac{N-1}{3}$] Byzantine SPs and 50% malicious SNs with up to 99.99% data availability.

Nowadays, crowdsourcing has evolved into a cost-efficient and scalable task execution paradigm that benefits both task requesters and workers. Task matching is a crucial crowdsourcing procedure for deciding the task execution quality, but security and privacy concerns arise as the crowdsourcing platform cannot be fully trusted. Existing privacy-aware task-matching schemes are limited to intra-platform central matching in the semi-honest model and coarse-grained keyword/location-based matching over one single attribute. Solutions supporting secure cross-platform and fine-grained task matching in the malicious model are urgently needed. In this paper, we first formally defined BFCrowd, a federated crowdsourcing system built on a consortium blockchain. BFCrowd aggregates multi-platform resources and enables decentralized and reliable cross-platform task matching using smart contracts, in the presence of malicious workers and platforms. Notably, we design a fully secure ciphertext-policy attribute-based encryption scheme with concealed access policies and user-side lightweight decryption, which thoroughly caters to the dual-side privacy demand and resource-limited workers and serves for fine-grained expressive task matching over multiple attributes. Moreover, it supports comparison over numerical attributes. Formal security analysis proves the desirable privacy guarantees in the standard model and collusion resistance. Extensive experiments implemented atop Hyperledger Fabric demonstrate both on-chain and off-chain performance.

Blockchain sharding enhances transaction throughput for vote-based blockchains while preserving system decentralization. Existing sharded blockchains can only maintain reasonable performance by tolerating a smaller magnitude of adversarial nodes compared to the upper-bound adversarial population of non-sharded ones. They lack adequate distinction of adversarial behaviors, with safety attacks posing catastrophic consequences and liveness attacks being more manageable. This paper presents WaveSpreader, a novel blockchain sharding protocol in a synchronous communication network that tolerates the same upper-bound adversarial population as non-sharded blockchains. It achieves a notable transaction throughput with the help of a robust mitigation method that effectively mitigates the liveness attacks. Our study provides a comprehensive security analysis and experimental results confirming the efficacy of WaveSpreader.

The proliferation of mobile internet and sharing economy has catalyzed the emergence of Ride-Sharing Services (RSSs) as a paradigm of spatial crowdsourcing in intelligent transportation. Compared with ride-hailing, RSSs present heightened challenges in security and service quality management due to bidirectional disclosure of trip plans and complex matching logic. Existing secure RSS solutions predominantly operate under semi-honest threat models or suffer from prohibitive computational complexity in service composition. However, ensuring public verifiability of matching outcomes is equally critical to prevent manipulation and ensure accountability in decentralized environments. Moreover, achieving a harmonious trade-off among privacy preservation, public verifiability, and matching efficiency remains an open challenge in RSS systems. This work proposes TBRS, a novelTEE-Blockchain powered privacy-awareRide-Sharing framework, which innovatively addresses three core challenges in service computing: (1) formalizing aninclusive matching modelthat extends traditional identical matching through trajectory region overlap analysis and direction alignment verification; (2) designing anIndex-Preserving Bloom Filter (IP-BF)coupled with Hilbert R-tree spatial indexing, achieving$O(\log n)$matching complexity through computational geometry optimization; (3) implementing a hybrid trusted execution environment via SGX-enhanced consortium blockchain withprivate smart contracts, ensuring verifiable service operations management under malicious threats. The framework demonstrates significant advancements in service performance management through systematic experiments: 2${\times }$$\sim$11${\times }$acceleration in on-chain service composition, 6×$\sim$33× improvement in off-chain computation efficiency, while maintaining over 99% service matching accuracy. These results signify that TBRS effectively breaks the efficiency bottleneck of existing privacy-preserving RSS solutions, making decentralized ride-sharing practical for deployment.

The rise of collaborative AI, particularly in distributed Mixture-of-Experts (MoE) systems, has created a critical challenge: how to ensure trust and transparency when aggregating proprietary models from different providers. To address this, we introduce a novel cryptographic protocol ZQ-WMA that enables verifiable and privacy-preserving online learning. Our method integrates zero-knowledge proofs with a quantized version of the Weighted Majority Algorithm, allowing a central aggregator to publicly prove it is honestly combining expert advice and updating weights according to the agreed-upon rules, all without revealing any confidential model parameters.This approach ensures that expert contributions are evaluated fairly and protects valuable intellectual property. Our analysis reveals that the quantization necessary for the zero-knowledge proofs can counter-intuitively enhance prediction accuracy, a phenomenon we attribute to the maximal entropy random walks. Furthermore, our benchmarks demonstrate the efficiency of this method, showing proof generation complexity less than 10% of a standard SHA256 hash function, with O(1) proof size and verification time. This work provides a practical and scalable framework for building trustworthy collaborative AI systems.

A decentralized identity system that can provide users with selfsovereign digital identities to facilitate complete control over their own data is paramount to Web 3.0.The account system on blockchain is an ideal archetype for realizing Web 3.0 decentralized identity.However, a disadvantage of such completely anonymous identity system is that users can create multiple accounts without authentication to obfuscate their activities on the blockchain.In particular, the current anonymous blockchain account system cannot accurately register the social relationships and interactions between real human users, given the amorphous mappings between users and blockchain identities.This work proposes zkBID, a zero-knowledge blockchain-account-based Web 3.0 decentralized identity scheme, to overcome endemic mistrust in blockchain account systems.zkBID links souls (blockchain accounts) to humans (users' personhood credentials) in a one-to-one manner to truly reflect the social relationships and interactions between humans on the blockchain.zkBID conceals the one-to-one relationships between blockchain accounts and users' personhood credentials for privacy protection using zero-knowledge proofs and linkable ring signatures.Thus, with zkBID, the users' blockchain accounts are credibly anonymous.Importantly, zkBID is fully decentralized: all user-related data are generated by users and verified by smart contracts on the blockchain.We implemented zkBID and built a blockchain test network for evaluation purposes.Our tests demonstrate the effectiveness of zkBID and suggest proper ways to configure zkBID system parameters.

In enterprize-scale Internet of Things, users need to query data by accessing the resource-constrained smart nodes. Such queries typically include data from one node (DON), and data from one catalog of multiple nodes (DOC). Traditional access control mechanisms often prove inadequate due to the lack of efficient policy management. Their authorization time for queries is linear with respect to the number of access control rules in the policy, which greatly impedes access granularity, efficiency, and scale. To address this issue, we propose FECAC, a fine-grained and efficient capability-based access control mechanism for DON and DOC access queries. Specifically, FECAC builds a policy matching tree structure by translating the rule into matching properties in the tree node, which avoids authorizing queries by traversing the entire rule collection. We then introduce an authorization scheme to match the elements of access requests in a top-down manner, and check the rules with the internal properties of the data queries sublinearly, which efficiently combines the requests of DOC and DON. Further, we give a concrete operation of FECAC from query authorization scheme to execute data querying based on capabilities. Finally, we demonstrate the improved and more stable evaluation efficiency of FECAC compared to existing schemes.

Abstract Smart contracts executed on blockchains are interactive programs where external actors generate events that trigger function invocations. Events can be emitted by participants asynchronously. However, some functionalities should be restricted to participants inhabiting specific roles in the system, which might be dynamically adjusted while the system evolves. We argue that current smart contract languages adopting imperative programming paradigms require additional complicated access control code. Furthermore, smart contracts are often developed and evolved independently and cannot share a joint access control policy. This makes it challenging to ensure the correctness of access control properties and to maintain correctness when the contracts are adapted. We propose using dynamic condition response (DCR) graphs for role‐based and declarative access control for smart contracts and techniques for test‐driven modelling and refinement of DCR graphs to support the safe design and evolution of smart contracts. We show that they allow for capturing and visualizing a form of dynamic access control where access rights evolve as the contract state progresses. Their use supports the straightforward declaration of access control rights, improved code auditing, test‐driven modelling, and safe evolution of smart contracts and improves users' understanding.

In crowdsensing, truth discovery (TD) has been extensively applied to resolve the data conflicts among publicly recruited workers and provide more reliable task data truths for task requesters. Data privacy and computation integrity are two major security concerns in TD due to the inherent untrustwor-thiness of the crowdsensing platform. Previous resolutions mostly focus on the design of privacy-preserving TD schemes while those leveraging blockchain to ensure TD integrity unfortunately sacrifice privacy or endure expensive on-chain overhead. In the presence of any misbehaviors, making TD integrity publicly and efficiently verifiable without compromising data privacy is imperative. This paper proposes p2STD, a Prlvacy-preservlng and Publicly verifiable scheme for generic Streaming TD (STD) in blockchain-enhanced crowdsensing. Unlike traditional TD in centralized crowdsensing, P2STD works in a decentralized architecture empowered by blockchain and fog, in which STD is jointly performed by fog nodes while the proof information is anchored to the blockchain for public verification. We take streaming confident-aware TD (S-CATD), the latest STD, as an instance and design secure aggregation protocols based on verifiable additive homomorphic secret sharing, concealing both task data and truth and generating publicly verifiable information. p2 STD is not specific to S-CATD and can be adapted to other iterative STD algorithms. Security analysis proves the desired privacy and public verifiability achieved in p2 STD. Experimental evaluations demonstrate that P2STD has high accuracy, lower computational and communication overhead, offering public verifiability with additional subtle gas cost than others.

Privacy-aware task allocation/matching has been an active research focus in crowdsourcing. However, existing studies focus on an honest-but-curious assumption and a single-attribute matching model. There is a lack of adequate attention paid to scheme designs against malicious behaviors and supporting user-side personalized task matching over multiple attributes. A few recent works employ blockchain and cryptographic techniques to decentralize the matching procedure with verifiable and privacy-preserving on-chain executions. However, they still bear expensive on-chain overhead. In this paper, we propose VP$^{2}$-Match, a blockchain-assisted (publicly) verifiable privacy-aware crowdsourcing task matching scheme with personalization. VP$^{2}$-Match extends symmetric hidden vector encryption for user-side expressive matching without compromising their privacy. It avoids costly on-chain matching by letting the blockchain only store evidence/proofs for public verifiability of the matching correctness and for enforcing fair interactions against misbehaviors. Specifically, we construct extended attribute sets and solve matching verification by an algorithmic reduction into subset verification with an accumulator for proof generation. Formal security proof and extensive comparison experiments on Ethereum demonstrate the provable security and better performance of VP$^{2}$-Match, respectively.

Mobile crowd sensing (MCS) has emerged as an increasingly popular sensing paradigm due to its cost-effectiveness. This approach relies on platforms to outsource tasks to participating workers when prompted by task publishers. Although incentive mechanisms have been devised to foster widespread participation in MCS, most of them focus only on static tasks (i.e., tasks for which the timing and type are known in advance) and do not protect the privacy of worker bids. In a dynamic and resource-constrained environment, tasks are often uncertain (i.e., the platform lacks a priori knowledge about the tasks) and worker bids may be vulnerable to inference attacks. This paper presents an incentive mechanism HERALD*, that takes into account the uncertainty and hidden bids of tasks without real-time constraints. Theoretical analysis reveals that HERALD* satisfies a range of critical criteria, including truthfulness, individual rationality, differential privacy, low computational complexity, and low social cost. These properties are then corroborated through a series of evaluations.

Smart contracts executed on blockchains are interactive programs where external actors generate events that trigger function invocations. Events can be emitted by participants asynchronously. However, some functionalities should be restricted to participants inhabiting specific roles in the system, which might be dynamically adjusted while the system evolves. We argue that current smart contract languages adopting imperative programming paradigms require additional complicated access control code. Furthermore, smart contracts are often developed independently and cannot share a joint access control policy. We propose to use Dynamic Condition Response Graphs for role-based and declarative access control for smart contracts. We show that they allow to capture and visualize a form of dynamic access control where access rights evolve as the contract state progresses. Their use supports straight-forward declaration of access control rights, improved code auditing, programming error reduction and improves users’ understanding of smart contracts.

Truth discovery is an effective and compelling approach to addressing data conflicts among different workers and offers more trustworthy truths to task requesters in crowdsourcing. Prior research either focused on studying more accurate truth discovery algorithms or aimed to protect data privacy from the centralized and honest-but-curious crowdsourcing platforms. They all overlooked the stronger threats from the malicious crowdsourcing platform (e.g., may return incorrectly estimated truths) and many critical issues inherited from centralization. This paper proposes a blockchain-based decentralized truth discovery scheme for crowdsourcing, with computation integrity guarantees against malicious participants and support for efficient processing of generic streaming data. We adopt the idea of hybrid storage and computations to ease the expensive on-chain cost. Workers are grouped for off-chain partial truth estimation and smart contracts are leveraged for on-chain final truth aggregation. To prevent any improper computations from malicious entities, we record the hashes of data and worker weights on-chain occasionally. Through theoretical analysis and extensive experiments over real-world and synthetic datasets implemented in Ethereum, we demonstrate that our scheme 1) achieves our reliability goals with certain privacy assurance; 2) exhibits a higher truth estimation accuracy than existing approaches and a lower gas consumption than the baseline.

Blockchain sharding splits a blockchain into several shards where consensus is reached at the shard level rather than over the entire blockchain. It improves transaction throughput and reduces the computational resources required of individual nodes. But a derivation of trustworthy consensus within a shard becomes an issue as the longest chain based mechanisms used in conventional blockchains can no longer be used. Instead, a vote-based consensus mechanism must be employed. However, existing vote-based Byzantine fault tolerance consensus protocols do not offer sufficient security guarantees for sharded blockchains. First, when used to support consensus where only one block is allowed at a time (binary consensus), these protocols are susceptible to progress-hindering attacks (i.e., unable to reach a consensus). Second, when used to support a stronger type of consensus where multiple concurrent blocks are allowed (strong consensus), their tolerance of adversary nodes is low. This article proposes a new consensus protocol to address all these issues. We call the new protocol MWPoW +, as its basic framework is based on the existing Multiple Winners Proof of Work (MWPoW) protocol but includes new mechanisms to address the issues mentioned previously. MWPoW+ is a vote-based protocol for strong consensus, asynchronous in consensus derivation but synchronous in communication. We prove that it can tolerate up to f < n /2 adversary nodes in a n-node system as if using a binary consensus protocol and does not suffer from progress-hindering attacks.

Blockchain sharding protocols randomly distribute nodes to different shards. They limit the quantity of shards to ensure that the adversary remains a minority inside each shard with a high probability. There can exist only a small number of shards. In this article, we propose a new sharding protocol that links the number of shards with the adversary population in real-time instead of a fixed upper-bounded population. The protocol is a two-phase design. First, several committee shards are constructed where the majority of nodes inside each are honest with high probability; then, each committee shard randomly splits into several worker shards with a high likelihood that at least one honest node is inside each. Each worker shard handles different transactions. Worker shard blocks that did not pass the unanimous voting are collected and voted by the committee shard using the majority voting. We show that (1) in the worst case (extremely unlikely) when all the transactions need to be handled by the committee shards, the transaction throughput and the data requirement only deteriorate to the same level as classical sharded blockchain; (2) when the worker shards handle most transactions, the overall transaction throughput is zoomed by two magnitudes securely while the data requirement for nodes remains at the same level.

The ubiquity of crowdsourcing has reshaped the static sensor-enabled data sensing paradigm with cost efficiency and flexibility. Still, most existing triangular crowdsourcing systems only work under the centralized trust assumption and suffer from various attacks mounted by malicious users. Although incorporating the emerging blockchain technology into crowdsourcing provides a possibility to mitigate some of the issues, how to concretely implement the crucial components and their functionalities in a verifiable and privacy-aware manner remains unaddressed. In this article, we present BRPC, a blockchain-based decentralized system for general crowdsourcing. BRPC integrates the confident-aware truth discovery algorithm to provide task requesters with reliable task truths while evaluating each worker’s data quality. To mitigate the biased evaluation of malicious requesters, we propose a privacy-aware verification protocol leveraging the threshold Paillier cryptosystem, with which a certain number of workers can collaboratively verify the evaluation results without knowing any sensory data. Furthermore, we define the three roles of a user and elaborate a comprehensive reputation evaluation model enforced by smart contracts for its trustworthy running. Financial and social incentives are both offered to motivate users’ honest participation. Finally, we implement a prototype of BRPC and deploy it on the Ethereum blockchain. Theoretical analyses and experiment results show its security and practicality.

Software evolution is an essential challenge for all software engineers, typically addressed solely using code versioning systems and language-specific code analysis tools. Most versioning systems view the evolution of a system as a directed acyclic graph of steps, with independent branches that could be merged. What these systems fail to provide is the ability to ensure stable APIs or that each subsequent evolution represents a cohesive extension yielding a valid system. Modular software evolution ensures that APIs remain stable, which is achieved by ensuring that only additional methods, fields, and data types are added, while treating existing modules through blackbox interfaces. Even with these restrictions, it must be possible to add new variations, fields, and methods without extensive duplication of prior module code. In contrast to most literature, our focus is on ensuring modular software evolution using mainstream object-oriented programming languages, instead of resorting to novel language extensions. We present a novel CoCo design pattern that supports type-safe covariantly overridden convert methods to transform earlier data type instances into their newest evolutionary representation to access operations that had been added later. CoCo supports both binary methods and producer methods. We validate and contrast our approach using a well-known compiler construction case study that other researchers have also investigated for modular evolution. Our resulting implementation relies on less boilerplate code, is completely type-safe, and allows clients to use normal object-oriented calling conventions. We also compare CoCo with existing approaches to the Expression Problem. We conclude by discussing how CoCo could change the direction of currently proposed Java language extensions to support closed-world assumptions about data types, as borrowed from functional programming.

We present a method for synthesizing compositions of mixins using type inhabitation in intersection types. First, recursively defined classes and mixins, which are functions over classes, are expressed as terms in a lambda calculus with records. Intersection types with records and record-merge are used to assign meaningful types to these terms without resorting to recursive types. Second, typed terms are translated to a repository of typed combinators. We show a relation between record types with record-merge and intersection types with constructors. This relation is used to prove soundness and partial completeness of the translation with respect to mixin composition synthesis. Furthermore, we demonstrate how a translated repository and goal type can be used as input to an existing framework for composition synthesis in bounded combinatory logic via type inhabitation. The computed result is a class typed by the goal type and generated by a mixin composition applied to an existing class.

The composition of features that interact with each other is challenging. Algebraic formalisms have been proposed by various authors to describe feature compositions and their interactions. The intention of feature compositions is the composition of fragments of documents of any kind to a product that fulfills users' requirements expressed by a feature selection. These modules often include code modules of typed programming languages whereas the proposed algebraic formalism is agnostic to types. This situation can lead to product code which is not type correct. In addition, types can carry semantic information on a program or module. We present a type system and connect it to an algebraic formalism thereby allowing automatic synthesis of feature compositions yielding well-typed programs.

For many software engineers, object-oriented frameworks represent the highest level of achievement in extensible design. The framework designers become experts in a specific application domain and design cooperating classes that impose specific responsibilities and collaborations for those seeking to extend the framework. In short, once a framework matures, it has complicated usage patterns that must be followed otherwise nothing works. Turning a framework into a software product line is challenging because of the difficulty in coding these complex behaviors and enabling the configuration of product line members using the framework. We propose to support this migration process by showing how to design a repository of modular units to synthesize member applications compositionally. These units are formalized using combinatory logic synthesis, a type-based approach to component-oriented synthesis. We demonstrate the feasibility of our approach with a Java-based product line for which we can automatically synthesize member applications.