A number of mobile applications have emerged that allow users to locate one another. However, people have expressed concerns about the privacy implications associated with this class of software, suggesting that broad adoption may only happen to the extent that these concerns are adequately addressed. In this article, we report on our work on PEOPLEFINDER, an application that enables cell phone and laptop users to selectively share their locations with others (e.g. friends, family, and colleagues). The objective of our work has been to better understand people's attitudes and behaviors towards privacy as they interact with such an application, and to explore technologies that empower users to more effectively and efficiently specify their privacy preferences (or "policies"). These technologies include user interfaces for specifying rules and auditing disclosures, as well as machine learning techniques to see if the system can help people manage their policies better. We present evaluations of these technologies in the context of one laboratory study and three field studies.

Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Keywords: phishing, email, filtering, semantic attacks, learning AbstractThere are an increasing number of emails purporting to be from a trusted entity that attempt to deceive users into providing account or identity information, commonly known as "phishing" emails. Traditional spam filters are not adequately detecting these undesirable emails, and this causes problems for both consumers and businesses wishing to do business online. From a learning perspective, this is a challenging problem. At first glance, the problem appears to be a simple text classification problem, but the classification is confounded by the fact that the class of "phishing" emails is nearly identical to the class of real emails. We propose a new method for detecting these malicious emails called PILFER. By incorporating features specifically designed to highlight the deceptive methods used to fool users, we are able to accurately classify over 92% of phishing emails, while maintaining a false positive rate on the order of 0.1%. These results are obtained on a dataset of approximately 860 phishing emails and 6950 non-phishing emails. The accuracy of PILFER on this dataset is significantly better than that of SpamAssassin, a widely-used spam filter.

There are an increasing number of emails purporting to be from a trusted entity that attempt to deceive users into providing account or identity information, commonly known as "phishing" emails. Traditional spam filters are not adequately detecting these undesirable emails, and this causes problems for both consumers and businesses wishing to do business online. From a learning perspective, this is a challenging problem. At first glance, the problem appears to be a simple text classification problem, but the classification is confounded by the fact that the class of "phishing" emails is nearly identical to the class of real emails. We propose a new method for detecting these malicious emails called PILFER. By incorporating features specifically designed to highlight the deceptive methods used to fool users, we are able to accurately classify over 92% of phishing emails, while maintaining a false positive rate on the order of 0.1%. These results are obtained on a dataset of approximately 860 phishing emails and 6950 non-phishing emails. The accuracy of PILFER on this dataset is significantly better than that of SpamAssassin, a widely-used spam filter.

Abstract

Class imbalance tends to cause inferior performance in data mining learners. Evolutionary sampling is a technique which seeks to counter this problem by using genetic algorithms to evolve a reduced sample of a complete dataset to train a classification model. Evolutionary sampling works to remove noisy and duplicate instances so that the sampled training data will produce a superior classifier. We propose this novel technique as a method to handle severe class imbalance in data mining. This paper presents our research into the the use of evolutionary sampling with C4.5 decision trees and compares the technique's performance with random undersampling.