Hasil Pencarian  ::  Simpan CSV :: Kembali

"The objective of this book is to provide the reader with a comprehensive survey of the topic compressed sensing in information retrieval and signal detection with privacy preserving functionality without compromising the performance of the embedding in terms of accuracy or computational efficiency. The reader is guided in exploring the topic by first establishing a shared knowledge about compressed sensing and how it is used nowadays. Then, clear models and definitions for its use as a cryptosystem and a privacy-preserving embedding are laid down, before tackling state-of-the-art results for both applications. The reader will conclude the book having learned that the current results in terms of security of compressed techniques allow it to be a very promising solution to many practical problems of interest. The book caters to a broad audience among researchers, scientists, or engineers with very diverse backgrounds, having interests in security, cryptography and privacy in information retrieval systems. Accompanying software is made available on the authors’ website to reproduce the experiments and techniques presented in the book. The only background required to the reader is a good knowledge of linear algebra, probability and information theory."

"

Privacy preserving data publishing (PPDP) merupakan bidang yang saat ini berkembang dengan fokus penelitian adalah mempertahankan data agar bersifat privat jika data tersebut dipublikasikan. Isu penting pada penelitian PPDP adalah meminimalkan nilai information loss yang diperoleh akibat proses penganoniman tabel mikrodata sehingga menjadi lebih privat. Berbagai model dan metode telah dikembangkan untuk mengatasi permasalahan tersebut. Model seperti k-anonymity, l-diversity, dan p-sensitive menjadi model dasar atas berkembangnya disiplin ilmu ini. Namun sebagian besar penelitian lebih banyak berfokus pada model untuk single sensitive attribute atau satu atribut sensitif pada tabel mikrodata. Padahal dalam dunia nyata atribut sensitif pada sebuah tabel bisa banyak atau multiple sensitive attributes. Penelitian yang membahas multiple-sensitive attributes pun masih banyak permasalahan yang belum terpecahkan karena hanya bertujuan untuk mengatasi satu permasalahan tertentu saja, misalnya untuk mengatasi serangan tertentu terhadap data. Sementara itu efek information loss kurang diperhatikan. Hal lain yang belum terlalu diperhatikan adalah bagaimana mendistribusikan nilai atribut sensitif ke seluruh grup data. Pendistribusian ini sangat penting untuk menghindari penumpukan data sensitif pada sebuah atau beberapa grup saja.

Penelitian ini berhasil mengusulkan dan mengevaluasi model PPDP dengan overlapped slicing pada multiple sensitive attributes dengan metode pendistribusian nilai atribut sensitif berupa simple distribution of sensitive values (SDSV) dan extended systematic clustering (ESC). Penelitian ini juga mengusukan sebuah pengukuran untuk menyempurnakan model pengukuran sebelumnya yaitu normalized and average discernibility metrics (NADM). Hasil dari penelitan ini menunjukkan overlapped slicing dengan tiga variasi metode untuk mencapaimodel tersebut memiliki tingkat information loss yang minimal dibandingkan dengan yang lain. Overlapped slicing dengan menggunakan variasi systematic clustering, SDSV, dan extended systematic clustering berhasil menghasilkan PPDP dengan nilai information loss yang kecil. Demikian juga dibandingkan dengan model lain yang menggunakan multiple sensitive attributes, overlapped slicing memiliki nilai information loss yang lebih kecil. Pada saat dijalankam dengan adult dataset, Nilai information loss yang telah dinormalkan untuk overlapped slicing adalah 0.25, sedangkan systematic clustering 0.625, SDSV 0.871, dan ESC 0,704. Dengan data bank marketing overlapped slicing menghasilkan nilai information loss yang dinormalkan sebesar 0.397, lebih baik daripada systematic clustering 0.441.

 

---

Privacy preserving data publishing (PPDP) is a field with research focus is in maintaining data to be private when the data is published. An important issue in PPDP is minimizing the information loss that is obtained due to the anonymization process to the microdata table so that it becomes more private. Various models and methods have been developed to overcome these problems. Models such as k-anonymity, l-diversity, and p-sensitive are the basic models for the development of this discipline. However, most studies focus on models for single sensitive attributes in microdata table. Yet in the real world, sensitive attributes on a table can be multiple sensitive attributes. There are still many problems in research that discusses multiple-sensitive attributes, and it still has not been solved because it only aims to overcome one particular problem for each research, for example to overcome certain attacks on data. Meanwhile the effect of information loss is less noticed. Another thing that has not been given much attention is how to distribute sensitive attribute values across data groups. This distribution is very important to avoid the accumulation of sensitive data on just one or a few groups.

This study successfully proposes and evaluates the PPDP model with overlapped slicing on multiple sensitive attributes and proposes methods for distributing sensitive attribute values namely, simple distribution of sensitive values (SDSV) and extended systematic clustering (ESC). This study also proposes a measurement to perfect the previous measurement model, normalized and average discernibility metrics (NADM). The results of this research show that overlapped slicing with three variation methods in achieving the model, has a minimalinformation loss compared to the others. Overlapped slicing by using systematic clustering, SDSV, and extended systematic clustering succeeded in producing PPDP with a small value of information loss. Likewise, compared to other models that use multiple sensitive attributes, overlapped slicing has a smaller information loss. When it is tested with adult dataset, the value of information loss that has been normalized for overlapped slicing is 0.25, while systematic clustering is 0.625, SDSV 0.871, and ESC is 0.704. With marketing bank dataset, it produces a normalized information loss value of 0.397, better than systematic clustering 0.441.

 

"

"An examination of the uses of data within a changing knowledge infrastructure, offering analysis and case studies from the sciences, social sciences, and humanities."Big Data" is on the covers of Science, Nature, the Economist, and Wired magazines, on the front pages of the Wall Street Journal and the New York Times. But despite the media hyperbole, as Christine Borgman points out in this examination of data and scholarly research, having the right data is usually better than having more data; little data can be just as valuable as big data. In many cases, there are no data -- because relevant data don't exist, cannot be found, or are not available. Moreover, data sharing is difficult, incentives to do so are minimal, and data practices vary widely across disciplines.Borgman, an often-cited authority on scholarly communication, argues that data have no value or meaning in isolation; they exist within a knowledge infrastructure -- an ecology of people, practices, technologies, institutions, material objects, and relationships. After laying out the premises of her investigation -- six "provocations" meant to inspire discussion about the uses of data in scholarship -- Borgman offers case studies of data practices in the sciences, the social sciences, and the humanities, and then considers the implications of her findings for scholarly practice and research policy. To manage and exploit data over the long term, Borgman argues, requires massive investment in knowledge infrastructures; at stake is the future of scholarship.--publisher."

"The book describes the emergence of big data technologies and the role of Spark in the entire big data stack. It compares Spark and Hadoop and identifies the shortcomings of Hadoop that have been overcome by Spark. The book mainly focuses on the in-depth architecture of Spark and our understanding of Spark RDDs and how RDD complements big data’s immutable nature, and solves it with lazy evaluation, cacheable and type inference. It also addresses advanced topics in Spark, starting with the basics of Scala and the core Spark framework, and exploring Spark data frames, machine learning using Mllib, graph analytics using Graph X and real-time processing with Apache Kafka, AWS Kenisis, and Azure Event Hub. It then goes on to investigate Spark using PySpark and R. Focusing on the current big data stack, the book examines the interaction with current big data tools, with Spark being the core processing layer for all types of data.The book is intended for data engineers and scientists working on massive datasets and big data technologies in the cloud. In addition to industry professionals, it is helpful for aspiring data processing professionals and students working in big data processing and cloud computing environments."