Motivated by many existing security and privacy applications, e.g., network traffic attribution, linkage attacks, private web search, and feature-based data de-anonymization, in this paper, we study the Feature-based Data Inferability (FDI) quantification problem. First, we conduct the FDI quantification under both naive and general data models from both a feature distance perspective and a feature distribution perspective. Our quantification explicitly shows the conditions to have a desired fraction of the target users to be Top-K inferable (K is an integer parameter). Then, based on our quantification, we evaluate the user inferability in two cases: network traffic attribution in network forensics and feature-based data de-anonymization. Finally, based on the quantification and evaluation, we discuss the implications of this research for existing feature-based inference systems.
In @cite_3 , Caliskan- presented a novel data de-anonymization attack to programmers leveraging the code stylometry. presented another stylometry-based de-anonymization attack in @cite_0 , by which they can identify anonymous authors of anonymous texts. In @cite_15 , Narayanan and Shmatikov presented a new class of statistical de-anonymization attacks to high-dimensional micro-data, e.g., recommendation data, transaction data, and so on. An off-line de-anonymization attack of bubble forms is presented in @cite_12 by
@cite_0 @cite_15 @cite_12 @cite_3
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Stylometry is a method for identifying anonymous authors of anonymous texts by analyzing their writing style. While stylometric methods have produced impressive results in previous experiments, we wanted to explore their performance on a challenging dataset of particular interest to the security research community. Analysis of underground forums can provide key information about who controls a given bot network or sells a service, and the size and scope of the cybercrime underworld. Previous analyses have been accomplished primarily through analysis of limited structured metadata and painstaking manual analysis. However, the key challenge is to automate this process, since this labor intensive manual approach clearly does not scale. We consider two scenarios. The first involves text written by an unknown cybercriminal and a set of potential suspects. This is standard, supervised stylometry problem made more difficult by multilingual forums that mix l33t-speak conversations with data dumps. In the second scenario, you want to feed a forum into an analysis engine and have it output possible doppelgangers, or users with multiple accounts. While other researchers have explored this problem, we propose a method that produces good results on actual separate accounts, as opposed to data sets created by artificially splitting authors into multiple identities. For scenario 1, we achieve 77 to 84 accuracy on private messages. For scenario 2, we achieve 94 recall with 90 precision on blogs and 85.18 precision with 82.14 recall for underground forum users. We demonstrate the utility of our approach with a case study that includes applying our technique to the Carders forum and manual analysis to validate the results, enabling the discovery of previously undetected doppelganger accounts. We present a new class of statistical de- anonymization attacks against high-dimensional micro-data, such as individual preferences, recommendations, transaction records and so on. Our techniques are robust to perturbation in the data and tolerate some mistakes in the adversary's background knowledge. We apply our de-anonymization methodology to the Netflix Prize dataset, which contains anonymous movie ratings of 500,000 subscribers of Netflix, the world's largest online movie rental service. We demonstrate that an adversary who knows only a little bit about an individual subscriber can easily identify this subscriber's record in the dataset. Using the Internet Movie Database as the source of background knowledge, we successfully identified the Netflix records of known users, uncovering their apparent political preferences and other potentially sensitive information. Fill-in-the-bubble forms are widely used for surveys, election ballots, and standardized tests. In these and other scenarios, use of the forms comes with an implicit assumption that individuals' bubble markings themselves are not identifying. This work challenges this assumption, demonstrating that fill-in-the-bubble forms could convey a respondent's identity even in the absence of explicit identifying information. We develop methods to capture the unique features of a marked bubble and use machine learning to isolate characteristics indicative of its creator. Using surveys from more than ninety individuals, we apply these techniques and successfully reidentify individuals from markings alone with over 50 accuracy. This bubble-based analysis can have either positive or negative implications depending on the application. Potential applications range from detection of cheating on standardized tests to attacks on the secrecy of election ballots. To protect against negative consequences, we discuss mitigation techniques to remove a bubble's identifying characteristics. We suggest additional tests using longitudinal data and larger datasets to further explore the potential of our approach in realworld applications. Source code authorship attribution is a significant privacy threat to anonymous code contributors. However, it may also enable attribution of successful attacks from code left behind on an infected system, or aid in resolving copyright, copyleft, and plagiarism issues in the programming fields. In this work, we investigate machine learning methods to de-anonymize source code authors of C C++ using coding style. Our Code Stylometry Feature Set is a novel representation of coding style found in source code that reflects coding style from properties derived from abstract syntax trees. Our random forest and abstract syntax tree-based approach attributes more authors (1,600 and 250) with significantly higher accuracy (94 and 98 ) on a larger data set (Google Code Jam) than has been previously achieved. Furthermore, these novel features are robust, difficult to obfuscate, and can be used in other programming languages, such as Python. We also find that (i) the code resulting from difficult programming tasks is easier to attribute than easier tasks and (ii) skilled programmers (who can complete the more difficult tasks) are easier to attribute than less skilled programmers.