相关论文: ShadowBlock: Efficient Dynamic Anonymous Blocklist…
Current blockchain-based reputation solutions for crowdsourcing fail to tackle the challenge of ensuring both efficiency and privacy without compromising the scalability of the blockchain. Developing an effective, transparent, and…
Entity matching seeks to identify data records over one or multiple data sources that refer to the same real-world entity. Virtually every entity matching task on large datasets requires blocking, a step that reduces the number of record…
Blockchains offer a useful abstraction: a trustworthy, decentralized log of totally ordered transactions. Traditional blockchains have problems with scalability and efficiency, preventing their use for many applications. These limitations…
Researchers have discovered a series of theoretical attacks against Bitcoin's Nakamoto consensus; the most damaging ones are selfish mining, double-spending, and consistency delay attacks. These attacks have one common cause: block…
In this paper, a new mathematical formulation for the problem of de-anonymizing social network users by actively querying their membership in social network groups is introduced. In this formulation, the attacker has access to a noisy…
We consider the privacy of interactions between individuals in a network. For many networks, while nodes are anonymous to outside observers, the existence of a link between individuals implies the possibility of one node revealing…
Clustering and analyzing on collected data can improve user experiences and quality of services in big data, IoT applications. However, directly releasing original data brings potential privacy concerns, which raises challenges and…
User demand for blocking advertising and tracking online is large and growing. Existing tools, both deployed and described in research, have proven useful, but lack either the completeness or robustness needed for a general solution.…
The Covid-19 pandemic has made individuals and organizations to rethink the way of handling identity verification and credentials sharing particularly in quarantined situations. In this study, we investigate the inefficiencies of…
In most electricity theft detection schemes, consumers' power consumption data is directly input into the detection center. Although it is valid in detecting the theft of consumers, the privacy of all consumers is at risk unless the…
Anomaly detection tools play a role of paramount importance in protecting networks and systems from unforeseen attacks, usually by automatically recognizing and filtering out anomalous activities. Over the years, different approaches have…
Permissioned blockchains are supported by identified but individually untrustworthy nodes, collectively maintaining a replicated ledger whose content is trusted. The Hyperledger Fabric permissioned blockchain system targets high-throughput…
Blockchain denial of service (BDoS) and selfish mining are the two most crucial attacks on blockchain technology. A classical DoS attack targets the computer network to limit, restrict, or stop accessing the system of authorized users which…
Proliferation of cryptocurrencies (e.g., Bitcoin) that allow pseudo-anonymous transactions, has made it easier for ransomware developers to demand ransom by encrypting sensitive user data. The recently revealed strikes of ransomware attacks…
Modern applications, such as social networking systems and e-commerce platforms are centered around using large-scale storage systems for storing and retrieving data. In the presence of concurrent accesses, these storage systems trade off…
Decentralized stochastic optimization is the basic building block of modern collaborative machine learning, distributed estimation and control, and large-scale sensing. Since involved data usually contain sensitive information like user…
Many algorithms have been proposed for fitting network models with communities, but most of them do not scale well to large networks, and often fail on sparse networks. Here we propose a new fast pseudo-likelihood method for fitting the…
In this paper, we develop BlockMarkchain, as a secure data market place, where individual data sellers can exchange certified data with buyers, in a secure environment, without any mutual trust among the parties, and without trusting on a…
Hierarchical federated learning (HFL) has emerged as an effective paradigm to enhance link quality between clients and the server. However, ensuring model accuracy while preserving privacy under unreliable communication remains a key…
We propose in this paper BlockHouse, a decentralized/P2P storage system fully based on private blockchains. Each participant can rent his unused storage in order to host data of other members. This system uses a dual Smart Contract and…