Transparent Personal Data Processing: The Road Ahead

The European General Data Protection Regulation defines a set of obligations for personal data controllers and processors. Primary obligations include: obtaining explicit consent from the data subject for the processing of personal data, providing full transparency with respect to the processing, and enabling data rectification and erasure (albeit only in certain circumstances). At the core of any transparency architecture is the logging of events in relation to the processing and sharing of personal data. The logs should enable verification that data processors abide by the access and usage control policies that have been associated with the data based on the data subject's consent and the applicable regulations. In this position paper, we: (i) identify the requirements that need to be satisfied by such a transparency architecture, (ii) examine the suitability of existing logging mechanisms in light of said requirements, and (iii) present a number of open challenges and opportunities

DF 2.0: Designing an automated, privacy preserving, and efficient digital forensic framework

The current state of digital forensic investigation is continuously challenged by the rapid technological changes, the increase in the use of digital devices (both the heterogeneity and the count), and the sheer volume of data that these devices could contain. Although it is not directly related to the performance of Digital Forensic Investigation process, preventing data privacy violations during the process is also a big challenge. The investigator gets full access to the forensic image including suspect\u27s private data which may be sensitive at times as well as entirely unrelated to the given case under investigation. With a notion that privacy preservation and completeness of investigation are contradicting to each other, the digital forensics researchers have provided solutions to address the above-stated challenges that either focus on the effectiveness of the investigation process or the data privacy preservation. However, a generalized approach that preserves data privacy by affecting neither the capabilities of the investigator nor the overall efficiency of the investigation process is still an open problem. In the current work, the authors have proposed a digital forensic framework that uses case information, case profile data and expert knowledge for automation of the digital forensic analysis process; utilizes machine learning for finding most relevant pieces of evidence; and preserves data privacy in such a way that the overall efficiency of the digital forensic investigation process increases without affecting the integrity and admissibility of the evidence. The framework improves validation to enhance transparency in the investigation process. The framework also uses a secure logging mechanism to capture investigation steps to achieve a higher level of accountability. Since the proposed framework introduces significant enhancements to the current investigative practices more like the next version of Digital Forensics, the authors named it `Digital Forensics 2.0\u27, or DF 2.0 in short

Blockchain for Genomics:A Systematic Literature Review

Human genomic data carry unique information about an individual and offer unprecedented opportunities for healthcare. The clinical interpretations derived from large genomic datasets can greatly improve healthcare and pave the way for personalized medicine. Sharing genomic datasets, however, pose major challenges, as genomic data is different from traditional medical data, indirectly revealing information about descendants and relatives of the data owner and carrying valid information even after the owner passes away. Therefore, stringent data ownership and control measures are required when dealing with genomic data. In order to provide secure and accountable infrastructure, blockchain technologies offer a promising alternative to traditional distributed systems. Indeed, the research on blockchain-based infrastructures tailored to genomics is on the rise. However, there is a lack of a comprehensive literature review that summarizes the current state-of-the-art methods in the applications of blockchain in genomics. In this paper, we systematically look at the existing work both commercial and academic, and discuss the major opportunities and challenges. Our study is driven by five research questions that we aim to answer in our review. We also present our projections of future research directions which we hope the researchers interested in the area can benefit from

Blockchain for Genomics:A Systematic Literature Review

Human genomic data carry unique information about an individual and offer unprecedented opportunities for healthcare. The clinical interpretations derived from large genomic datasets can greatly improve healthcare and pave the way for personalized medicine. Sharing genomic datasets, however, pose major challenges, as genomic data is different from traditional medical data, indirectly revealing information about descendants and relatives of the data owner and carrying valid information even after the owner passes away. Therefore, stringent data ownership and control measures are required when dealing with genomic data. In order to provide secure and accountable infrastructure, blockchain technologies offer a promising alternative to traditional distributed systems. Indeed, the research on blockchain-based infrastructures tailored to genomics is on the rise. However, there is a lack of a comprehensive literature review that summarizes the current state-of-the-art methods in the applications of blockchain in genomics. In this paper, we systematically look at the existing work both commercial and academic, and discuss the major opportunities and challenges. Our study is driven by five research questions that we aim to answer in our review. We also present our projections of future research directions which we hope the researchers interested in the area can benefit from