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crowdsourcing

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RSA-250 Factored

RSA-250 has been factored.

This computation was performed with the Number Field Sieve algorithm,
using the open-source CADO-NFS software.

The total computation time was roughly 2700 core-years, using Intel Xeon Gold 6130 CPUs as a reference (2.1GHz):

RSA-250 sieving: 2450 physical core-years
RSA-250 matrix: 250 physical core-years

The computation involved tens of thousands of machines worldwide, and was completed in a few months.

News article. On the factoring challenges.

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New Techniques in Fake Reviews

Research paper: “Automated Crowdturfing Attacks and Defenses in Online Review Systems.”

Abstract: Malicious crowdsourcing forums are gaining traction as sources of spreading misinformation online, but are limited by the costs of hiring and managing human workers. In this paper, we identify a new class of attacks that leverage deep learning language models (Recurrent Neural Networks or RNNs) to automate the generation of fake online reviews for products and services. Not only are these attacks cheap and therefore more scalable, but they can control rate of content output to eliminate the signature burstiness that makes crowdsourced campaigns easy to detect.

Using Yelp reviews as an example platform, we show how a two phased review generation and customization attack can produce reviews that are indistinguishable by state-of-the-art statistical detectors. We conduct a survey-based user study to show these reviews not only evade human detection, but also score high on “usefulness” metrics by users. Finally, we develop novel automated defenses against these attacks, by leveraging the lossy transformation introduced by the RNN training and generation cycle. We consider countermeasures against our mechanisms, show that they produce unattractive cost-benefit tradeoffs for attackers, and that they can be further curtailed by simple constraints imposed by online service providers.

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