相关论文: Cybercasing 2.0: You Get What You Pay For

Differential privacy is a rigorous mathematical framework for evaluating and protecting data privacy. In most existing studies, there is a vulnerable assumption that records in a dataset are independent when differential privacy is applied.…

It is well known that the usefulness of a machine learning model is due to its ability to generalize to unseen data. This study uses three popular cyberbullying datasets to explore the effects of data, how it's collected, and how it's…

With the advance of technology, Criminal Justice agencies are being confronted with an increased need to investigate crimes perpetuated partially or entirely over the Internet. These types of crime are known as cybercrimes. In order to…

The research identifies association rules that can inform marketing strategies and enhance operational efficiency. A structured methodology is applied to extract and interpret meaningful relationships within transactional data, emphasizing…

Data forging attacks provide counterfactual proof that a model was trained on a given dataset, when in fact, it was trained on another. These attacks work by forging (replacing) mini-batches with ones containing distinct training examples…

Data markets serve as crucial platforms facilitating data discovery, exchange, sharing, and integration among data users and providers. However, the paramount concern of privacy has predominantly centered on protecting privacy of data…

Prior work has extensively studied misinformation related to news, politics, and health, however, misinformation can also be about technological topics. While less controversial, such misinformation can severely impact companies'…

Understanding the legal status of IP addresses is complex. In Europe, the General Data Protection Regulation (GDPR) is supposed to have leveraged the legal status of IP addresses as personal data, but recent decisions from the European…

Building upon previous research in honeynets and simulations, we present efforts from a two-and-a-half-year study using a representative simulation to collect cybersecurity data. Unlike traditional honeypots or honeynets, our experiment…

Cybersecurity analysts work on large communication data sets to perform investigative analysis by painstakingly going over thousands of email conversations to find potential scamming activities and the network of cyber scammers.…

Individuals lack oversight over systems that process their data. This can lead to discrimination and hidden biases that are hard to uncover. Recent data protection legislation tries to tackle these issues, but it is inadequate. It does not…

The problem of private information "leakage" (inadvertently or by malicious design) from the myriad large centralized searchable data repositories drives the need for an analytical framework that quantifies unequivocally how safe private…

We analyze digital markets where a monopolist platform uses data to match multiproduct sellers with heterogeneous consumers who can purchase both on and off the platform. The platform sells targeted ads to sellers that recommend their…

This article provides a quantitative analysis of privacy-compromising mechanisms on 1 million popular websites. Findings indicate that nearly 9 in 10 websites leak user data to parties of which the user is likely unaware; more than 6 in 10…

Various studies on consumer purchasing behaviors have been presented and used in real problems. Data mining techniques are expected to be a more effective tool for analyzing consumer behaviors. However, the data mining method has…

This chapter examines the policy implications of behavioural sciences insights for the regulation of privacy on the Internet, by focusing in particular on behavioural targeting. This marketing technique involves tracking people's online…

We hear it all too often in the media: an organization is attacked, its data, often containing personally identifying information, is made public, and a hacking group emerges to claim credit. In this excerpt, we discuss how such groups…

Data mining has been widely used to identify potential customers for a new product or service. In this article is done a study of previous work relating to the application of data mining methodologies for software projects, specifically for…

Releasing Web query logs which contain valuable information for research or marketing, can breach the privacy of search engine users. Therefore rendering query logs to limit linking a query to an individual while preserving the data…

A data marketplace is an online venue that brings data owners, data brokers, and data consumers together and facilitates commoditisation of data amongst them. Data pricing, as a key function of a data marketplace, demands quantifying the…