Defending against sophisticated cyber threats demands strategic allocation of limited security resources across complex network infrastructures. When the defender has limited defensive resources, the complexity of coordinating honeypot placements across hundreds of nodes grows exponentially. In this paper, we present a multi-attacker Bayesian Stackelberg framework modeling concurrent adversaries attempting to breach a directed network of system components. Our approach uniquely characterizes each adversary through distinct target preferences, exploit capabilities, and associated costs, while enabling defenders to strategically deploy honeypots at critical network positions. By integrating a multi-follower Stackelberg formulation with dynamic Bayesian belief updates, our framework allows defenders to continuously refine their understanding of attacker intentions based on actions detected through Intrusion Detection Systems (IDS). Experimental results show that the proposed method prevents attack success within a few rounds and scales well up to networks of 500 nodes with more than 1,500 edges, maintaining tractable run times.

The modern pharmaceutical supply chain lacks transparency and traceability, resulting in alarming rates of counterfeit products entering the market. These illegitimate products cause harm to end users and wreak havoc on the supply chain itself, costing billions of dollars in profit loss. In this paper, in response to the Drug Supply Chain Security Act (DSCSA), we introduce Janus, a novel pharmaceutical track-and-trace system that utilizes blockchain and cloning-resistant hologram tags to prevent counterfeits from entering the pharmaceutical supply chain. We designed a multi-quorum consensus protocol that achieves load balancing across the network. We perform a security analysis to show robustness against various threats and attacks. The implementation of Janus proves that the system is fair, scalable, and resilient.

Blockchain is an emerging distributed and decentralized technology that promises to revolutionize the healthcare sector by securely storing and maintaining incorruptible electronic health record data. Consensus protocols are at the core of blockchain technology. They establish security and integrity in the system by ensuring that the majority of miners are in agreement on all transactions and blocks added to the distributed ledger. While many consensus protocols have been proposed, most of them require heavy computation and are not scalable. In this work, we propose a novel permissioned consensus protocol, named ACCORD, a multi-leader (quorum-based) protocol that achieves fork-resistance, robustness, and scalability. To achieve this, ACCORD consists of three distinct components: (1) an asynchronous quorum selection procedure to designate the creators of future blocks, (2) a block creation protocol run by the quorum to prevent omissions in the presence of honest quorum members, and (3) a decentralized arbitration protocol to ensure consensus by voting. Additionally, we implemented the protocol and conducted experiments to demonstrate scalability, robustness, and fairness.

One of the most harmful consequences of social media is the rise of cyberbullying, which tends to be more sinister than traditional bullying, given that online records typically live on the Internet for quite a long time and are hard to control. In this article, we present a three-phase algorithm, called BullyNet, for detecting cyberbullies on Twitter social network. We exploit bullying tendencies by proposing a robust method for constructing a cyberbullying signed network (SN). We analyze tweets to determine their relation to cyberbullying while considering the context in which the tweets exist in order to optimize their bullying score. We also propose a centrality measure to detect cyberbullies from a cyberbullying SN and show that it outperforms other existing measures. We experiment on a data set of 5.6 million tweets, and our results show that the proposed approach can detect cyberbullies with high accuracy while being scalable with respect to the number of tweets.

Since its inception a decade ago, Bitcoin and its underlying blockchain technology have been garnering interest from a large spectrum of financial institutions. Although it encompasses a currency, a payment method, and a ledger, Bitcoin as it currently stands does not support bitcoins lending. In this paper, we present a platform called ZeroLender for peer-to-peer lending in Bitcoin. Our protocol utilizes zero-knowledge proofs to achieve unlinkability between lenders and borrowers while securing payments in both directions against potential malicious behaviour of the ZeroLender as well as the lenders, and prove by simulation that our protocol is privacy-preserving. Based on our experiments, we show that the runtime and transcript size of our protocol scale linearly with respect to the number of lenders and repayments.

This work is to enhance existing fine-grained access control to support a more expressive access policy over arithmetic operation results. We aim to enable data owners to flexibly bind a user's identity with his/her authorized access target according to a given access control policy, which indicates how a piece of data obfuscated by different noises. To this end, we design a cryptographic primitive that decouples the noisy data to two components, one associated with user identity, and the other one shared and dynamically changes, with the composite of these two components evaluated and revealed at user sides. The security of our scheme is formally proven using game based approach. We implement our system on a commercial cloud platform and use extensive experiments to validate its functionality and performance.

Voting is a fundamental part of democratic systems; it gives individuals in a community the faculty to voice their opinion. In recent years, voter turnout has diminished while concerns regarding integrity, security, and accessibility of current voting systems have escalated. E-voting was introduced to address those concerns; however, it is not cost-effective and still requires full supervision by a central authority. The blockchain is an emerging, decentralized, and distributed technology that promises to enhance different aspects of many industries. Expanding e-voting into blockchain technology could be the solution to alleviate the present concerns in e-voting. In this paper, we propose a blockchain-based voting system, named BroncoVote, that preserves voter privacy and increases accessibility, while keeping the voting system transparent, secure, and cost-effective. BroncoVote implements a university-scaled voting framework that utilizes Ethereum’s blockchain and smart contracts to achieve voter administration and auditable voting records. In addition, BroncoVote utilizes a few cryptographic techniques, including homomorphic encryption, to promote voter privacy. Our implementation was deployed on Ethereum’s Testnet to demonstrate usability, scalability, and efficiency.

Over the years, email has evolved into one of the most widely used communication channels for both individuals and organizations. However, despite near ubiquitous use in much of the world, current information technology standards do not place emphasis on email security. Not until recently, webmail services such as Yahoo's mail and Google's gmail started to encrypt emails for privacy protection. However, the encrypted emails will be decrypted and stored in the service provider's servers. If the servers are malicious or compromised, all the stored emails can be read, copied and altered. Thus, there is a strong need for end-to-end (E2E) email encryption to protect email user's privacy. In this paper, we present a certificateless one-way group key agreement protocol with the following features, which are suitable to implement E2E email encryption: (1) certificateless and thus there is no key escrow problem and no public key certificate infrastructure is required; (2) one-way group key agreement and thus no back-and-forth message exchange is required; and (3) n-party group key agreement (not just 2- or 3-party). This paper also provides a security proof for the proposed protocol using "proof by simulation". Finally, efficiency analysis of the protocol is presented at the end of the paper.

Bitcoin exchanges function like banks, securely holding customers' bitcoins on their behalf. Several exchanges have suffered catastrophic losses with customers permanently losing their savings. A proof of solvency demonstrates cryptographically that the exchange controls sufficient reserves to settle each customer's account. We introduce Provisions, a privacy-preserving proof of solvency whereby an exchange does not have to disclose its Bitcoin addresses; total holdings or liabilities; or any information about its customers. We also propose an extension which prevents exchanges from colluding to cover for each other's losses. We have implemented Provisions and it offers practical computation times and proof sizes even for a large Bitcoin exchange with millions of customers.

In the last decade, several approaches concerning private data release for data mining have been proposed. Data mashup, on the other hand, has recently emerged as a mechanism for integrating data from several data providers. Fusing both techniques to generate mashup data in a distributed environment while providing privacy and utility guarantees on the output involves several challenges. That is, how to ensure that no unnecessary information is leaked to the other parties during the mashup process, how to ensure the mashup data is protected against certain privacy threats, and how to handle the high-dimensional nature of the mashup data while guaranteeing high data utility. In this paper, we present Fusion, a privacy-preserving multi-party protocol for data mashup with guaranteed LKC-privacy for the purpose of data mining. Experiments on real-life data demonstrate that the anonymous mashup data provide better data utility, the approach can handle high dimensional data, and it is scalable with respect to the data size.

Data-as-a-Service (DaaS) mashup enables data providers to dynamically integrate their data on demand depending on consumers' requests. Utilizing DaaS mashup, however, involves some challenges. Mashing up data from multiple sources to answer a consumer's request might reveal sensitive information and thereby compromise the privacy of individuals. Moreover, data integration of arbitrary DaaS providers might not always be sufficient to answer incoming requests. In this paper, we provide a cloud-based framework for privacy-preserving DaaS mashup that enables secure collaboration between DaaS providers for the purpose of generating an anonymous dataset to support data mining. Experiments on real-life data demonstrate that our DaaS mashup framework is scalable and can efficiently and effectively satisfy the data privacy and data mining requirements specified by the DaaS providers and the data consumers.

Extracting association rules helps data owners to unveil hidden patterns from their data for the purpose of analyzing and predicting the behavior of their clients. However, mining association rules in a distributed environment is not a trivial task due to privacy concerns. Data owners are interested in collaborating with each other to mine association rules on a global level; however, they are concerned that sensitive information related to the individuals involved in their database might get compromised during the mining process. In this paper, we formulate and address the problem of answering association rules queries in a distributed environment such that the mining process is confidential and the results are differentially private. We propose a privacy-preserving distributed association rules mining approach, named DARM, where global strong association rules are determined in a confidential way, and the results returned satisfy ε-differential privacy. We conduct our experiments on real-life data, and show that our approach can efficiently answer association rules queries and is scalable with increasing data records.