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1. Adversarial Robustness of Similarity-Based Link Prediction

   Zhou, Kai; Michalak, Tomasz; Vorobeychik, Yevgeniy (January 2019, IEEE International Conference on Data Mining)

   Link prediction is one of the fundamental problems in social network analysis. A common set of techniques for link prediction rely on similarity metrics which use the topology of the observed subnetwork to quantify the likelihood of unobserved links. Recently, similarity metrics for link prediction have been shown to be vulnerable to attacks whereby observations about the network are adversarially modified to hide target links. We propose a novel approach for increasing robustness of similarity-based link prediction by endowing the analyst with a restricted set of reliable queries which accurately measure the existence of queried links. The analyst aims to robustly predict a collection of possible links by optimally allocating the reliable queries. We formalize the analyst problem as a Bayesian Stackelberg game in which they first choose the reliable queries, followed by an adversary who deletes a subset of links among the remaining (unreliable) queries by the analyst. The analyst in our model is uncertain about the particular target link the adversary attempts to hide, whereas the adversary has full information about the analyst and the network. Focusing on similarity metrics using only local information, we show that the problem is NP-Hard for both players, and devise two principled and efficient approaches for solving it approximately. Extensive experiments with real and synthetic networks demonstrate the effectiveness of our approach. 

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2. Identifying a Psychometric Profile for Vulnerability Assessment Professionals: Talent Identification to Support Career Assessment

   Crosby, M. (January 2020, South Dakota law review)

   An inter-collegiate research team completed initial research analysis of 166 professional cybersecurity workers from government and industry to identify talent profiles aligned four roles within the Protect and Defend (PD) NICE Workforce Framework: Cybersecurity Defense Analyst, Cybersecurity Defense Infrastructure Responder, Cybersecurity Incident Responder and Cybersecurity Vulnerability Assessment Analyst. Anonymized data collected from multiple teams with performance assessments to build a statistically validated profiles of high potential PD cybersecurity candidates. The World of Work Inventory (WOWI) a multi-dimensional on-line career tool, assesses career training potential, job satisfaction indicators and career interests. Anonymized, aggregated ranked data described profiles of existing high performing candidates working in the field. Utilization of a statistically validated methodology to identify cybersecurity talent at different phases of an individual’s career life cycle supports recruitment of high potential talent from diverse backgrounds to increase the numbers of candidates entering cybersecurity education and training programs. 

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3. Drove: Tracking Execution Results of Workflows on Large Datasets

   Sadeem Alsudais (October 2022, PhD Workshop at VLDB 2022)

   Data analytics using workflows is an iterative process, in which an analyst makes many iterations of changes, such as additions, deletions, and alterations of operators and their links. In many cases, the analyst wants to compare these workflow versions and their execution results to help in deciding the next iterations of changes. Moreover, the analyst needs to know which versions produced undesired results to avoid refining the workflow in those versions. To enable the analyst to get an overview of the workflow versions and their results, we introduce Drove, a framework that manages the end-to-end lifecycle of constructing, refining, and executing workflows on large data sets and provides a dashboard to monitor these execution results. In many cases, the result of an execution is the same as the result of a prior execution. Identifying such equivalence between the execution results of different workflow versions is important for two reasons. First, it can help us reduce the storage cost of the results by storing equivalent results only once. Second, stored results of early executions can be reused for future executions with the same results. Existing tools that track such executions are geared towards small-scale data and lack the means to reuse existing results in future executions. In Drove, we reason the semantic equivalence of the workflow versions to reduce the storage space and reuse the materialized results. 

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4. Tessera: Discretizing Data Analysis Workflows on a Task Level

   https://doi.org/10.1145/3411764.3445728  

   Yan, Jing Nathan; Gu, Ziwei; Rzeszotarski, Jeffrey M (May 2021, CHI '21: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems)

   Researchers have investigated a number of strategies for capturing and analyzing data analyst event logs in order to design better tools, identify failure points, and guide users. However, this remains challenging because individual- and session-level behavioral differences lead to an explosion of complexity and there are few guarantees that log observations map to user cognition. In this paper we introduce a technique for segmenting sequential analyst event logs which combines data, interaction, and user features in order to create discrete blocks of goal-directed activity. Using measures of inter-dependency and comparisons between analysis states, these blocks identify patterns in interaction logs coupled with the current view that users are examining. Through an analysis of publicly available data and data from a lab study across a variety of analysis tasks, we validate that our segmentation approach aligns with users’ changing goals and tasks. Finally, we identify several downstream applications for our approach. 

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5. The Fienberg Problem: How to Allow Human Interactive Data Analysis in the Age of Differential Privacy

   https://doi.org/10.29012/jpc.687  

   Dwork, Cynthia; Ullman, Jonathan (December 2018, Journal of Privacy and Confidentiality)

   null (Ed.)

   Differential Privacy is a popular technology for privacy-preserving analysis of large datasets. DP is powerful, but it requires that the analyst interact with data only through a special interface; in particular, the analyst does not see raw data, an uncomfortable situation for anyone trained in classical statistical data analysis. In this note we discuss the (overly) simple problem of allowing a  trusted analyst to choose an ``"interesting" statistic for popular release (the actual computation of the chosen statistic will be carried out in a differentially private way). 

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