Hubungan antara amalan pengurusan rantaian bekalan hijau (GSCM) dan prestasi rantaian bekalan di sektor pembuatan, Batu Pahat, Johor

Pola pertumbuhan ekonomi telah meningkatkan tahap penggunaan tenaga dan bahan-bahan dan memberi kesan kepada kemampanan alam sekitar. Kemakmuran ekonomi bagi penduduk bandar yang lebih besar, lebih banyak membawa kepada pembaziran pengeluaran. Hal ini berlaku kerana penghasilan sisa dipengaruhi oleh proses perindustrian. Sisa dari sektor pembuatan boleh mendatangkan ancaman terhadap bekalan air, kesihatan awam dan alam sekitar melalui penyebaran bakteria lalu mengakibatkan pencemaran sumber air. Air yang tercemar mungkin mengandungi kepekatan bahan-bahan pencemaran yang tinggi yang dihasilkan oleh kilang-kilang dan akan datang kembali kepada pengguna melalui kitaran air sebagai sumber utama untuk kegunaan harian. Oleh itu, pengguna terdedah kepada pelbagai penyakit dan sekali gus boleh menjejaskan tahap kesihatan mereka (Khairul, Rahman & Ho, 2011). Isu alam persekitaran merupakan isu yang kian mencabar kepada organisasi perniagaan pada masa kini. Pemanasan global, pengurangan kualiti udara dan pencemaran air merupakan beberapa contoh kesan alam sekitar yang boleh dikaitkan dengan aktiviti yang selaras dengan rantaian bekalan organisasi. Pada 9 April 2009, Perdana Menteri telah mengumumkan pembentukan Kementerian Tenaga, Teknologi Hijau dan Air (KeTTHA) bagi menggantikan Kementerian Tenaga, Air dan Komunikasi. Teknologi Hijau merujuk kepada pembangunan dan aplikasi produk, peralatan serta sistem untuk memelihara alam sekitar dan alam semulajadi dan meminimumkan atau mengurangkan kesan negatif daripada aktiviti manusia

Privacy and Confidentiality in an e-Commerce World: Data Mining, Data Warehousing, Matching and Disclosure Limitation

The growing expanse of e-commerce and the widespread availability of online databases raise many fears regarding loss of privacy and many statistical challenges. Even with encryption and other nominal forms of protection for individual databases, we still need to protect against the violation of privacy through linkages across multiple databases. These issues parallel those that have arisen and received some attention in the context of homeland security. Following the events of September 11, 2001, there has been heightened attention in the United States and elsewhere to the use of multiple government and private databases for the identification of possible perpetrators of future attacks, as well as an unprecedented expansion of federal government data mining activities, many involving databases containing personal information. We present an overview of some proposals that have surfaced for the search of multiple databases which supposedly do not compromise possible pledges of confidentiality to the individuals whose data are included. We also explore their link to the related literature on privacy-preserving data mining. In particular, we focus on the matching problem across databases and the concept of ``selective revelation'' and their confidentiality implications.Comment: Published at http://dx.doi.org/10.1214/088342306000000240 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Privacy Preserving Utility Mining: A Survey

In big data era, the collected data usually contains rich information and hidden knowledge. Utility-oriented pattern mining and analytics have shown a powerful ability to explore these ubiquitous data, which may be collected from various fields and applications, such as market basket analysis, retail, click-stream analysis, medical analysis, and bioinformatics. However, analysis of these data with sensitive private information raises privacy concerns. To achieve better trade-off between utility maximizing and privacy preserving, Privacy-Preserving Utility Mining (PPUM) has become a critical issue in recent years. In this paper, we provide a comprehensive overview of PPUM. We first present the background of utility mining, privacy-preserving data mining and PPUM, then introduce the related preliminaries and problem formulation of PPUM, as well as some key evaluation criteria for PPUM. In particular, we present and discuss the current state-of-the-art PPUM algorithms, as well as their advantages and deficiencies in detail. Finally, we highlight and discuss some technical challenges and open directions for future research on PPUM.Comment: 2018 IEEE International Conference on Big Data, 10 page