This handbook covers Electronic Medical Record (EMR) systems, which enable the storage, management, and sharing of massive amounts of demographic, diagnosis, medication, and genomic information. It presents privacy-preserving methods for medical data, ranging from laboratory test results to doctors' comments. The reuse of EMR data can greatly benefit medical science and practice, but must be performed in a privacy-preserving way according to data sharing policies and regulations. Written by world-renowned leaders in this field, each chapter offers a survey of a research direction or a solution to problems in established and emerging research areas. The authors explore scenarios and techniques for facilitating the anonymization of different types of medical data, as well as various data mining tasks. Other chapters present methods for emerging data privacy applications and medical text de-identification, including detailed surveys of deployed systems. A part of the book is devoted to legislative and policy issues, reporting on the US and EU privacy legislation and the cost of privacy breaches in the healthcare domain. This reference is intended for professionals, researchers and advanced-level students interested in safeguarding medical data.
Aris Gkoulalas-Divanis received the BS from the University of Ioannina (2003), the MS from the University of Minnesota (2005) and the PhD from the University of Thessaly (2009), all in Computer Science. His PhD dissertation was awarded the Certificate of Recognition and Honorable Mention in the 2009 ACM SIGKDD Dissertation Award. From 2009 to 2010, he was appointed as a postdoctoral research fellow in the Dept. of Biomedical Informatics, Vanderbilt University, working on medical data privacy. In 2010, he joined IBM Research-Zurich, as a Research Staff Member. Since 2012, he is working in the Smarter Cities Technology Center of IBM Research-Ireland, leading research in the area of data privacy and anonymization. Aris is a regular reviewer for several prestigious journals and serves in the program committee of major conferences. He has co-authored/co-edited 4 Springer books in the areas of data anonymization, knowledge hiding, and large-scale data mining.
Grigorios Loukides is an Assistant Professor in the School of Computer Science & Informatics at Cardiff University and a Royal Academy of Engineering Research Fellow. His research interests lie broadly in the field of data management with a focus on privacy. His recent research investigates theoretical and practical aspects of data privacy, including algorithmic design, optimization, and formal modeling, and explores applications in healthcare and business. He has received 4 best paper awards, including an award from the American Medical Informatics Association (AMIA) Annual Symposium, 2009. He obtained a Diploma in Computer Science (2005) from University of Crete, Greece, and a PhD in Computer Science (2009) from Cardiff University, UK.
