Options
Ayala-Rivera, Vanessa
Preferred name
Ayala-Rivera, Vanessa
Official Name
Ayala-Rivera, Vanessa
Research Output
Now showing 1 - 10 of 18
- PublicationA Systematic Comparison and Evaluation of k-Anonymization Algorithms for Practitioners(Transactions on Data Privacy, 2014)
; ; ; The vast amount of data being collected about individuals has brought new challenges in protecting their privacy when this data is disseminated. As a result, Privacy-Preserving Data Publishing has become an active research area, in which multiple anonymization algorithms have been proposed. However, given the large number of algorithms available and limited information regarding their performance, it is difficult to identify and select the most appropriate algorithm given a particular publishing scenario, especially for practitioners. In this paper, we perform a systematic comparison of three well-known k-anonymization algorithms to measure their efficiency (in terms of resources usage) and their effectiveness (in terms of data utility). We extend the scope of their original evaluation by employing a more comprehensive set of scenarios: different parameters, metrics and datasets. Using publicly available implementations of those algorithms, we conduct a series of experiments and a comprehensive analysis to identify the factors that influence their performance, in order to guide practitioners in the selection of an algorithm. We demonstrate through experimental evaluation, the conditions in which one algorithm outperforms the others for a particular metric, depending on the input dataset and privacy requirements. Our findings motivate the necessity of creating methodologies that provide recommendations about the best algorithm given a particular publishing scenario.1840 - PublicationEnhancing the Utility of Anonymized Data by Improving the Quality of Generalization Hierarchies(Transactions on Data Privacy, 2017-04)
; ; ; ; The dissemination of textual personal information has become an important driver of innovation. However, due to the possible content of sensitive information, this data must be anonymized. A commonly-used technique to anonymize data is generalization. Nevertheless, its effectiveness can be hampered by the Value Generalization Hierarchies (VGHs) used as poorly-specified VGHs can decrease the usefulness of the resulting data. To tackle this problem, in our previous work we presented the Generalization Semantic Loss (GSL), a metric that captures the quality of categorical VGHs in terms of semantic consistency and taxonomic organization. We validated the accuracy of GSL using an intrinsic evaluation with respect to a gold standard ontology. In this paper, we extend our previous work by conducting an extrinsic evaluation of GSL with respect to the performance that VGHs have in anonymization (using data utility metrics). We show how GSL can be used to perform an a priori assessment of the VGHs¿ effectiveness for anonymization. In this manner, data publishers can quantitatively compare the quality of various VGHs and identify (before anonymization) those that better retain the semantics of the original data. Consequently, the utility of the anonymized datasets can be improved without sacrificing the privacy goal. Our results demonstrate the accuracy of GSL, as the quality of VGHs measured with GSL strongly correlates with the utility of the anonymized data. Results also show the benefits that an a priori VGH assessment strategy brings to the anonymization process in terms of time-savings and a reduction in the dependency on expert knowledge. Finally, GSL also proved to be lightweight in terms of computational resources.353 - PublicationImproving the Testing of Java Garbage Collection Through an Efficient Benchmark GenerationGarbage Collection (GC) is a core feature of multiple modern technologies (e.g., Java, Android). On one hand, it offers significant software engineering benefits over explicitly memory management, like preventing most types of memory leaks. On the other hand, GC is a known cause of performance degradation. However, it is considerably challenging to understand its exact impact on the overall application performance. This is because the non-deterministic nature of GC makes very complex to properly model it and evaluate its performance impacts. To help tackling these problems, we present an engine to generate realistic GC benchmarks by enabling to effectively capture the GC/memory behaviours experienced by real-world Java applications. We also demonstrate, through a comprehensive experimental evaluation, how such benchmarks can be useful to strengthen the evaluation of GC-related advancements.
526 - PublicationTowards an Efficient Performance Testing Through Dynamic Workload Adaptation(Springer, 2019-10-08)
; ; ; Performance testing is a critical task to ensure an acceptable user experience with software systems, especially when there are high numbers of concurrent users. Selecting an appropriate test workload is a challenging and time-consuming process that relies heavily on the testers’ expertise. Not only are workloads application-dependent, but also it is usually unclear how large a workload must be to expose any performance issues that exist in an application. Previous research has proposed to dynamically adapt the test workloads in real-time based on the application behavior. By reducing the need for the trial-and-error test cycles required when using static workloads, dynamic workload adaptation can reduce the effort and expertise needed to carry out performance testing. However, such approaches usually require testers to properly configure several parameters in order to be effective in identifying workload-dependent performance bugs, which may hinder their usability among practitioners. To address this issue, this paper examines the different criteria needed to conduct performance testing efficiently using dynamic workload adaptation. We present the results of comprehensively evaluating one such approach, providing insights into how to tune it properly in order to obtain better outcomes based on different scenarios. We also study the effects of varying its configuration and how this can affect the results obtained.234 - PublicationOntology-Based Quality Evaluation of Value Generalization Hierarchies for Data Anonymization(2014-09)
; ; ; In privacy-preserving data publishing, approaches using Value Generalization Hierarchies (VGHs) form an important class of anonymization algorithms. VGHs play a key role in the utility of published datasets as they dictate how the anonymization of the data occurs. For categorical attributes, it is imperative to preserve the semantics of the original data in order to achieve a higher utility. Despite this, semantics have not being formally considered in the specification of VGHs. Moreover, there are no methods that allow the users to assess the quality of their VGH. In this paper, we propose a measurement scheme, based on ontologies, to quantitatively evaluate the quality of VGHs, in terms of semantic consistency and taxonomic organization, with the aim of producing higher-quality anonymizations. We demonstrate, through a case study, how our evaluation scheme can be used to compare the quality of multiple VGHs and can help to identify faulty VGHs.227 - PublicationAutomatic Construction of Generalization Hierarchies for Publishing Anonymized DataConcept hierarchies are widely used in multiple fields to carry out data analysis. In data privacy, they are known as Value Generalization Hierarchies (VGHs), and are used by generalization algorithms to dictate the data anonymization. Thus, their proper specification is critical to obtain anonymized data of good quality. The creation and evaluation of VGHs require expert knowledge and a significant amount of manual effort, making these tasks highly error-prone and timeconsuming. In this paper we present AIKA, a knowledge-based framework to automatically construct and evaluate VGHs for the anonymization of categorical data. AIKA integrates ontologies to objectively create and evaluate VGHs. It also implements a multi-dimensional reward function to tailor the VGH evaluation to different use cases. Our experiments show that AIKA improved the creation of VGHs by generating VGHs of good quality in less time than when manually done. Results also showed how the reward function properly captures the desired VGH properties.
429Scopus© Citations 2 - PublicationCOCOA: A Synthetic Data Generator for Testing Anonymization Techniques(Springer, 2016-09-16)
; ; ; Conducting extensive testing of anonymization techniques is critical to assess their robustness and identify the scenarios where they are most suitable. However, the access to real microdata is highly restricted and the one that is publicly-available is usually anonymized or aggregated; hence, reducing its value for testing purposes. In this paper, we present a framework (COCOA) for the generation of realistic synthetic microdata that allows to define multi-attribute relationships in order to preserve the functional dependencies of the data. We prove how COCOA is useful to strengthen the testing of anonymization techniques by broadening the number and diversity of the test scenarios. Results also show how COCOA is practical to generate large datasets.1000Scopus© Citations 8 - PublicationOne Size Does Not Fit All: In-Test Workload Adaptation for Performance Testing of Enterprise Applications(ACM, 2018-04-13)
; ; ; ; Carrying out proper performance testing is considerably challenging .In particular, the identification of performance issues, as well as their root causes, is a time-consuming and complex process which typically requires several iterations of tests (as this type of issue scan depend on the input workloads), and heavily relies on human expert knowledge. To improve this process, this paper presents an automated approach (that extends some of our previous work) to dynamically adapt the workload (used by a performance testing tool) during the test runs. As a result, the performance issues of the tested application can be revealed more quickly; hence, identifying them with less effort and expertise. Our experimental evaluation has assessed the accuracy of the proposed approach and the time savings that it brings to testers. The results have demonstrated the benefits of the approach by achieving a significant decrease in the time invested in performance testing (without compromising the accuracy of the test results), while introducing a low overhead in the testing environment.417Scopus© Citations 6 - PublicationA Requirements-based Approach for the Evaluation of Emulated IoT SystemsThe Internet of Things (IoT) has become a major technological revolution. Evaluating any IoT advancements comprehensively is critical to understand the conditions under which they can be more useful, as well as to assess the robustness and efficiency of IoT systems to validate them before their deployment in real life. Nevertheless, the creation of an appropriate IoT test environment is a difficult, effort-intensive, and expensive task; typically requiring a significant amount of human effort and physical hardware to build it. To tackle this problem, emulation tools to test IoT devices have been proposed. However, there is a lack of systematic approaches for evaluating IoT emulation environments. In this paper, we present a requirements-based framework to enable the systematic evaluation of the suitability of an emulated IoT environment to fulfil the requirements that secure the quality of an adequate test environment for IoT.
438Scopus© Citations 1 - PublicationA unified approach to automate the usage of plagiarism detection tools in programming courses(IEEE, 2017-08-25)
; ; ; Plagiarism in programming assignments is an extremely common problem in universities. While there are many tools that automate the detection of plagiarism in source code, users still need to inspect the results and decide whether there is plagiarism or not. Moreover, users often rely on a single tool (using it as "gold standard" for all cases), which can be ineffective and risky. Hence, it is desirable to make use of several tools to complement their results. However, various limitations exist in these tools that make their usage a very time-consuming task, such as the need of manually analyzing and correlating their multiple outputs. In this paper, we propose an automated system that addresses the common usage limitations of plagiarism detection tools. The system automatically manages the execution of different plagiarism tools and generates a consolidated comparative visualization of their results. Consequently, the user can make better-informed decisions about potential plagiarisms. Our experimental results show that the effort and expertise required to use plagiarism detection tools is significantly reduced, while the probability of detecting plagiarism is increased. Results also show that our system is lightweight (in terms of computational resources), proving it is practical for real-world usage.476Scopus© Citations 3