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Known Sample Attacks on Relation Preserving data transformations

IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING 2020年第2期17卷 443-450页

作者： Kaplan, Emre Gursoy, Mehmet Emre Nergiz, Mehmet Ercan Saygin, Yucel Sabanci Univ Fac Engn & Nat Sci TR-34956 Istanbul Turkey Georgia Inst Technol Coll Comp Atlanta GA 30332 USA

Many data mining applications such as clustering and $k$k-NN search rely on distances and relations in the data. Thus, distance preserving transformations, which perturb the data but retain records' distances, have emerged as a prominent privacy protection method. In this paper, we present a novel attack on a generalized form of distance preserving transformations, called relation preserving transformations. Our attack exploits not the exact distances between data, but the relationships between the distances. We show that an attacker with few known samples (4 to 10) and direct access to relations can retrieve unknown data records with more than 95 percent precision. In addition, experiments demonstrate that simple methods of noise addition or perturbation are not sufficient to prevent our attack, as they decrease precision by only 10 percent.

关键词： data mining security and privacy data transformations known sample attacks protection

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Assessing the Effect of data transformations on Test Suite Compilation 18

Assessing the Effect of Data Transformations on Test Suite C...

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12th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM) as part of the Empirical Software Engineering International Week (ESEIW)

作者： Stratis, Panagiotis Yaneva, Vanya Rajan, Ajitha Univ Edinburgh Sch Informat Edinburgh Midlothian Scotland

ISBN: (纸本)9781450358231

Background. The requirements and responsibilities assumed by software has increasingly rendered it to be large and complex. Testing to ensure that software meets all its requirements and is free from failures is a difficult and time-consuming task that necessitates the use of large test suites, containing many tests. Large test suites result in a corresponding increase in the size of the test code that sets up, exercises and verifies the tests. Time needed to compile and optimise the test code becomes prohibitive for large test code sizes. Aims. In this paper we demonstrate for the first time optimisations to speedup compilation of test code. Reducing the compilation time of test code for large and complex systems will allow additional tests to be compiled and executed, while also enabling more frequent and rigorous testing. Methods. We propose transformations that reduce the number of instructions in the test code, which in turn reduces compilation time. Using two well known compilers, GCC and Clang, we conduct empirical evaluations using subject programs from industry standard benchmarks and an industry provided program. We evaluate compilation speedup, execution time, scalability and correctness of the proposed test code transformation. Results. Our approach resulted in significant compilation speedups in the range of 1.3x to 69x. Execution of the test code was just as fast with our transformation when compared to the original while also preserving correctness of execution. Finally, our experiments show that the gains in compilation time allow significantly more tests to be included in a single binary, improving scalability of test code compilation. Conclusions. The proposed transformation results in faster test code compilation for all the programs in our experiment, with more significant speedups for larger case studies and larger numbers of tests. As systems get more complex requiring frequent and extensive testing, we believe our approach provides a safe and

关键词： data transformations Testing Compilers

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Approximate prediction intervals in the original units when fitting models using data transformations

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QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2018年第1期34卷 53-65页

作者： Perry, Marcus B. Univ Alabama Dept Informat Syst Stat & Management Sci 300 Alston Hall Box 870226 Tuscaloosa AL 35487 USA

In this paper, we consider prediction interval estimation in the original units of observation after fitting a linear model to an appropriately transformed response variable. We assume that the residuals obtained from fitting the linear model in the transformed space are iid zero-mean normal random variables, at least approximately. We discuss the bias in the retransformed mean and derive a reduced-bias estimator for the kth moment of the original response, given settings of the design variables. This is then used to compute reduced-bias estimates for the mean and variance of the untransformed response at various locations in design space. We then exploit a well-known probability inequality, along with our proposed moment estimator, to construct an approximate 100(1 - alpha)% prediction interval on the original response, given settings of the design factors. We used Monte Carlo simulation to evaluate the performance of the proposed prediction interval estimator relative to 2 commonly used alternatives. Our results suggest the proposed method is often the better alternative when the sample size is small and/or when the underlying model is misspecified. We illustrate the application of our new method by applying it to a real experimental data set obtained from the literature, where machine tool life was studied as a function of various machining parameters.

关键词： data transformations linear models prediction intervals retransformation bias

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Declarative data transformations for Linked data Generation: The Case of DBpedia 1

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14th International Semantic Web Conference (ESWC)

作者： De Meester, Ben Maroy, Wouter Dimou, Anastasia Verborgh, Ruben Mannens, Erik Univ Ghent IMEC IDLab Ghent Belgium

ISBN: (数字)9783319584515

ISBN: (纸本)9783319584515

Mapping languages allow us to define how Linked data is generated from raw data, but only if the raw data values can be used as is to form the desired Linked data. Since complex data transformations remain out of scope for mapping languages, these steps are often implemented as custom solutions, or with systems separate from the mapping process. The former data transformations remain case-specific, often coupled with the mapping, whereas the latter are not reusable across systems. In this paper, we propose an approach where data transformations (i) are defined declaratively and (ii) are aligned with the mapping languages. We employ an alignment of data transformations described using the Function Ontology (FnO) and mapping of data to Linked data described using the RDF Mapping Language (RML). We validate that our approach can map and transform DBpedia in a declaratively defined and aligned way. Our approach is not case-specific: data transformations are independent of their implementation and thus interoperable, while the functions are decoupled and reusable. This allows developers to improve the generation framework, whilst contributors can focus on the actual Linked data, as there are no more dependencies, neither between the transformations and the generation framework nor their implementations.

关键词： data transformations FnO Linked data generation RML

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Synthesizing data Structure transformations from Input-Output Examples 15

Synthesizing Data Structure Transformations from Input-Outpu...

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36th ACM SIGPLAN Conference on Programming Language Design and Implementation

作者： Feser, John K. Chaudhuri, Swarat Dillig, Isil Rice Univ Houston TX 77251 USA UT Austin Austin TX 78712 USA

ISBN: (纸本)9781450334686

We present a method for example-guided synthesis of functional programs over recursive data structures. Given a set of input-output examples, our method synthesizes a program in a functional language with higher-order combinators like map and fold. The synthesized program is guaranteed to be the simplest program in the language to fit the examples. Our approach combines three technical ideas: inductive generalization, deduction, and enumerative search. First, we generalize the input-output examples into hypotheses about the structure of the target program. For each hypothesis, we use deduction to infer new input/output examples for the missing subexpressions. This leads to a new subproblem where the goal is to synthesize expressions within each hypothesis. Since not every hypothesis can be realized into a program that fits the examples, we use a combination of best-first enumeration and deduction to search for a hypothesis that meets our needs. We have implemented our method in a tool called lambda(2), and we evaluate this tool on a large set of synthesis problems involving lists, trees, and nested data structures. The experiments demonstrate the scalability and broad scope of lambda(2). A highlight is the synthesis of a program believed to be the world's earliest functional pearl.

关键词： Program synthesis programming by example data transformations search-based synthesis automated deduction

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Declarative RDF graph generation from heterogeneous (semi-)structured data: A systematic literature review

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JOURNAL OF WEB SEMANTICS 2023年 75卷

作者： Van Assche, Dylan Delva, Thomas Haesendonck, Gerald Heyvaert, Pieter De Meester, Ben Dimou, Anastasia Ghent Univ Imec IDLab Dept Elect & Informat Syst Technolpk Zwijnaarde 122 B-9052 Ghent Belgium KU Leuven Leuven AI Flanders Make Dept Comp Sci Jan Pieter Nayerlaan 5 B-2860 St Katelijne Waver Belgium

More and more data in various formats are integrated into knowledge graphs. However, there is no overview of existing approaches for generating knowledge graphs from heterogeneous (semi -)structured data, making it difficult to select the right one for a certain use case. To support better decision making, we study the existing approaches for generating knowledge graphs from heterogeneous (semi-)structured data relying on mapping languages. In this paper, we investigated existing mapping languages for schema and data transformations, and corresponding materialization and virtualization systems that generate knowledge graphs. We gather and unify 52 articles regarding knowledge graph generation from heterogeneous (semi-)structured data. We assess 15 characteristics on mapping languages for schema transformations, 5 characteristics for data transformations, and 14 characteristics for systems. Our survey paper provides an overview of the mapping languages and systems proposed the past two decades. Our work paves the way towards a better adoption of knowledge graph generation, as the right mapping language and system can be selected for each use case.(c) 2022 Published by Elsevier B.V.

关键词： Knowledge graph construction Schema transformations data transformations Survey Declarative

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Computationally Efficient Tests for Multivariate Skew Normality

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JOURNAL OF STATISTICAL THEORY AND PRACTICE 2025年第3期19卷 1-22页

作者： Gonzalez-Estrada, Elizabeth Colegio Postgrad Programa Estadist & Ciencia Datos Carr Mexico Texcoco Km 36-5 Mexico City 56230 Mexico

This manuscript addresses the problem of testing the multivariate skew normal distribution hypothesis when parameters are unknown. data transformations to observations with approximately univariate normal distribution are employed to propose four computationally efficient tests for this composite hypothesis. The Shapiro-Wilk test is applied for assessing the normality of the transformed data. Existing normal approximations are used for computing the critical constants of this test, avoiding the use of expensive resampling techniques like parametric bootstrap. The size and power properties of these procedures are studied by means of Monte Carlo simulation, including different parameter configurations under the null and alternative hypotheses. Two of the proposed procedures have pretty good control of the type I error probability under the considered settings. Real data examples are included to illustrate the utility of the tests.

关键词： Shapiro-Wilk test for normality Goodness-of-fit Test for the gamma distribution data transformations Type I error probability

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Using Kernels to Harness the Complexity of Big data Applications

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INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS 2022年第3期31卷

作者： Wah, Benjamin W. Chinese Univ Hong Kong Dept Comp Sci & Engn Shatin Hong Kong Peoples R China

This paper presents the concept of kernels to address the complexity of solving big-data applications. Their solution strategies often require evaluating domain-dependent subspaces on the big data and selecting the best result. As the data space in these problems is so vast that it is infeasible to scan the data once, we need domain-specific methods for identifying promising and manageable subspaces with a high density of solutions before looking for individual ones. To this end, we introduce kernels to represent some properties of the statistical quality, average density, or probability of solutions in a subspace to prune subspaces with suboptimal kernels. We classify various past approaches based on their analysis methods and illustrate each by an example. Our results confirm that kernels can effectively harness the complexity of solving big-data applications.

关键词： Kernels big-data applications data distributions pruning strategies data transformations

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LLMs with User-defined Prompts as Generic data Operators for Reliable data Processing

LLMs with User-defined Prompts as Generic Data Operators for...

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2023 IEEE International Conference on Big data, Bigdata 2023

作者： Ma, Luyi Thakurdesai, Nikhil Chen, Jiao Xu, Jianpeng Korpeoglu, Evren Kumar, Sushant Achan, Kannan Walmart Global Tech Personalization Team SunnyvaleCA United States

ISBN: (纸本)9798350324457

data processing is one of the fundamental steps in machine learning pipelines to ensure data quality. Majority of the applications consider the user-defined function (UDF) design pattern for data processing in databases. Although the UDF design pattern introduces flexibility, reusability and scalability, the increasing demand on machine learning pipelines brings three new challenges to this design pattern - not low-code, not dependency-free and not knowledge-aware. To address these challenges, we propose a new design pattern that large language models (LLMs) could work as a generic data operator (LLM-GDO) for reliable data cleansing, transformation and modeling with their human-compatible performance. In the LLM-GDO design pattern, user-defined prompts (UDPs) are used to represent the data processing logic rather than implementations with a specific programming language. LLMs can be centrally maintained so users don't have to manage the dependencies at the run-time. Fine-tuning LLMs with domain-specific data could enhance the performance on the domain-specific tasks which makes data processing knowledge-aware. We illustrate these advantages with examples in different data processing tasks. Furthermore, we summarize the challenges and opportunities introduced by LLMs to provide a complete view of this design pattern for more discussions. © 2023 IEEE.

关键词： data Cleansing data Modeling data transformations Design Pattern Large Language Models

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IEEE P1687.1: Extending the Network Boundaries for Test

IEEE P1687.1: Extending the Network Boundaries for Test

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IEEE International Test Conference (ITC)

作者： Laisne, Michael Crouch, Alfred Portolan, Michele Keim, Martin Von Staudt, Hans Martin Van Treuren, Bradford G. Rearick, Jeff Zuo, Songlin Dialog Semicond Santa Clara CA 95101 USA Amida Technol Solut Inc Austin TX USA Univ Grenoble Alpes TIMA CNRS Grenoble INP Grenoble France Siemens Digital Ind Software Wilsonville OR USA Dialog Semicond Kirchheim unter Teck Germany VT Enterprises Consulting Serv Lambertville NJ USA Adv Micro Devices Inc Ft Collins CO USA Facebook San Diego CA USA

ISBN: (数字)9781665462709

ISBN: (纸本)9781665462709

Modern integrated circuits used in automotive, industrial, consumer, and IoT (Internet of Things) applications often have limited pin counts. Despite this, these devices can be complex, with significant digital and analog content. These features require rigorous testing to address the quality and reliability requirements of these applications. Because of their limited pin count, a TAP controller interface is often not an option or the TAP controller interface itself may not be an option for other reasons, such as die area. Further, the increasing number of IP based designs, along with the increasing availability of IP cores can result in an IP being integrated deep inside a design. Designers, DfT engineers, and test engineers are facing increasing challenges with the integration and testability of IP in their chips. They also have the task of having to test through interfaces that may change depending on the needs of the customers. IEEE P1687.1 is working on processes that will provide a means for automating communications through non-TAP interfaces, making these types of products ideally suited for the techniques currently under consideration for standardization. This paper, reviews the latest topics being discussed by the working group, including the framework for communicating through these interfaces. It discusses how proto buffers (protobufs) can be used to help bridge the gap between a host controller, like an ATE, and an IJTAG network. The approach leverages the concept of Remote Procedural Calls (RPCs) from the IT space and the integer bit vector (intbv) concept from myHDL to support communications through interfaces and between subroutines handling retargeting and handling control of the interface. A model is presented for evaluating the options and various criteria are reviewed with respect to the framework. The paper also explores debug concerns and potential security issues and how those can be addressed.

关键词： IJTAG 1687.1 ATE Reconfigurable Scan Networks Secure Access Authentication Automated Test Environments data transformations IoT callback retargeting

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