Joining up health and bioinformatics: e-science meets e-health

CLEF (Co-operative Clinical e-Science Framework) is an MRC sponsored project in the e-Science programme that aims to establish methodologies and a technical infrastructure forthe next generation of integrated clinical and bioscience research. It is developing methodsfor managing and using pseudonymised repositories of the long-term patient histories whichcan be linked to genetic, genomic information or used to support patient care. CLEF concentrateson removing key barriers to managing such repositories ? ethical issues, informationcapture, integration of disparate sources into coherent ?chronicles? of events, userorientedmechanisms for querying and displaying the information, and compiling the requiredknowledge resources. This paper describes the overall information flow and technicalapproach designed to meet these aims within a Grid framework

SISTEM PENUNJANG KEPUTUSAN PENERIMAAN RETRIBUSI PADA DINAS PERMUKIMAN DAN KEBERSIHAN MENGGUNAKAN ALGORITMA NAÏVE BAYES

Dinas Pemukiman Dan Kebersihan Kabupaten Pangkep bertugas menjaga kebersihan dan kenyamanan Kabupaten Pangkep sehingga kota Pangkep menjadi kota yang bersih dan indah. Tugas dan tanggung jawab ada pada Dinas Pemukiman Dan Kebersihan untuk bersama-sama warganya menjaga kota Pangkep supaya tetap bersih dan indah.Sampah yang dihasilkan oleh masyarakat bisa menghasilkan retribusi terhadap Kabupaten pangkep yang nantinya di kelola menjadi APBD Kabupaten Pangkep setiap tahunnya, selama ini Dinas Pemukiman dan Kebersihan Kabupaten Pangkep dalam mengambil retribusi sampah masih digunakan cara-cara tradisional dengan menggunakan Sistem Semikomputer atau manual.Sehingga penerimaan retribusi sampah rawan di selewengkan oleh oknum yang tidak bertanggung jawab maka dari itu penulis mencoba membuat suatu Sistem yang nantinya penerimaan retribusi sampah bisa dikontrol oleh admin. Dengan sistem komputerisasi menggunakan program aplikasi visual basic dan menggunakan algoritma Naïve Bayes dengan akurasi 77, 50 % Dimana nantinya pegawai yang bertugas mengangkut sampah dari rumah tangga, restoran, rumah sakit dan pasar sesuai dengan biaya yang sudah ditentukan oleh masyarakat dan pemerintah dalam hal ini Dinas Pemukiman dan Kebersihan Kabupaten Pangkep. Pegawai yang bertugas akan membagikan karcis ke setiap rumah tangga, restoran, rumah sakit dan pasar setiap harinya sehingga berapa karcis yang dibawa oleh pegawai nantinya akan di hitung karcis yang tinggal dikali dengan harga karcis berdasarkan warna karcisnya yang sudah ditetapkan oleh Dinas Pemukiman dan Kebersihan Kabupaten Pangkep

Methods for the de-identification of electronic health records for genomic research

Electronic health records are increasingly being linked to DNA repositories and used as a source of clinical information for genomic research. Privacy legislation in many jurisdictions, and most research ethics boards, require that either personal health information is de-identified or that patient consent or authorization is sought before the data are disclosed for secondary purposes. Here, I discuss how de-identification has been applied in current genomic research projects. Recent metrics and methods that can be used to ensure that the risk of re-identification is low and that disclosures are compliant with privacy legislation and regulations (such as the Health Insurance Portability and Accountability Act Privacy Rule) are reviewed. Although these methods can protect against the known approaches for re-identification, residual risks and specific challenges for genomic research are also discussed

Privacy in the Genomic Era

Genome sequencing technology has advanced at a rapid pace and it is now possible to generate highly-detailed genotypes inexpensively. The collection and analysis of such data has the potential to support various applications, including personalized medical services. While the benefits of the genomics revolution are trumpeted by the biomedical community, the increased availability of such data has major implications for personal privacy; notably because the genome has certain essential features, which include (but are not limited to) (i) an association with traits and certain diseases, (ii) identification capability (e.g., forensics), and (iii) revelation of family relationships. Moreover, direct-to-consumer DNA testing increases the likelihood that genome data will be made available in less regulated environments, such as the Internet and for-profit companies. The problem of genome data privacy thus resides at the crossroads of computer science, medicine, and public policy. While the computer scientists have addressed data privacy for various data types, there has been less attention dedicated to genomic data. Thus, the goal of this paper is to provide a systematization of knowledge for the computer science community. In doing so, we address some of the (sometimes erroneous) beliefs of this field and we report on a survey we conducted about genome data privacy with biomedical specialists. Then, after characterizing the genome privacy problem, we review the state-of-the-art regarding privacy attacks on genomic data and strategies for mitigating such attacks, as well as contextualizing these attacks from the perspective of medicine and public policy. This paper concludes with an enumeration of the challenges for genome data privacy and presents a framework to systematize the analysis of threats and the design of countermeasures as the field moves forward

Scalable and approximate privacy-preserving record linkage

Record linkage, the task of linking multiple databases with the aim to identify records that refer to the same entity, is occurring increasingly in many application areas. Generally, unique entity identifiers are not available in all the databases to be linked. Therefore, record linkage requires the use of personal identifying attributes, such as names and addresses, to identify matching records that need to be reconciled to the same entity. Often, it is not permissible to exchange personal identifying data across different organizations due to privacy and confidentiality concerns or regulations. This has led to the novel research area of privacy-preserving record linkage (PPRL). PPRL addresses the problem of how to link different databases to identify records that correspond to the same real-world entities, without revealing the identities of these entities or any private or confidential information to any party involved in the process, or to any external party, such as a researcher. The three key challenges that a PPRL solution in a real-world context needs to address are (1) scalability to largedatabases by efficiently conducting linkage; (2) achieving high quality of linkage through the use of approximate (string) matching and effective classification of the compared record pairs into matches (i.e. pairs of records that refer to the same entity) and non-matches (i.e. pairs of records that refer to different entities); and (3) provision of sufficient privacy guarantees such that the interested parties only learn the actual values of certain attributes of the records that were classified as matches, and the process is secure with regard to any internal or external adversary. In this thesis, we present extensive research in PPRL, where we have addressed several gaps and problems identified in existing PPRL approaches. First, we begin the thesis with a review of the literature and we propose a taxonomy of PPRL to characterize existing techniques. This allows us to identify gaps and research directions. In the remainder of the thesis, we address several of the identified shortcomings. One main shortcoming we address is a framework for empirical and comparative evaluation of different PPRL solutions, which has not been studied in the literature so far. Second, we propose several novel algorithms for scalable and approximate PPRL by addressing the three main challenges of PPRL. We propose efficient private blocking techniques, for both three-party and two-party scenarios, based on sorted neighborhood clustering to address the scalability challenge. Following, we propose two efficient two-party techniques for private matching and classification to address the linkage quality challenge in terms of approximate matching and effective classification. Privacy is addressed in these approaches using efficient data perturbation techniques including k-anonymous mapping, reference values, and Bloom filters. Finally, the thesis reports on an extensive comparative evaluation of our proposed solutions with several other state-of-the-art techniques on real-world datasets, which shows that our solutions outperform others in terms of all three key challenges