Digital forensics as a service: Game on

The big data era has a high impact on forensic data analysis. Work is done in speeding up the processing of large amounts of data and enriching this processing with new techniques. Doing forensics calls for specific design considerations, since the processed data is incredibly sensitive. In this paper we explore the impact of forensic drivers and major design principles like security, privacy and transparency on the design and implementation of a centralized digital forensics service.     

A study on the forensic mechanisms of VoIP attacks: Analysis and digital evidence

This paper discusses the use of communication technology to commit crimes, including crime facts and crime techniques. The analysis focuses on the security of voice over Internet protocol (VoIP), a prevention method against VoIP call attack and the attention points for setting up an Internet phone. The importance of digital evidence and digital forensics are emphasised. This paper provides the VoIP digital evidence forensics standard operating procedures (DEFSOP) to help police organisations and establishes an experimental platform to simulate phone calls, hacker attacks and forensic data. Finally, this paper provides a general discussion of a digital evidence strategy that includes VoIP for crime investigators who are interested in digital evidence forensics.     

Impacts of increasing volume of digital forensic data: A survey and future research challenges

A major challenge to digital forensic analysis is the ongoing growth in the volume of data seized and presented for analysis. This is a result of the continuing development of storage technology, including increased storage capacity in consumer devices and cloud storage services, and an increase in the number of devices seized per case. Consequently, this has led to increasing backlogs of evidence awaiting analysis, often many months to years, affecting even the largest digital forensic laboratories. Over the preceding years, there has been a variety of research undertaken in relation to the volume challenge. Solutions posed range from data mining, data reduction, increased processing power, distributed processing, artificial intelligence, and other innovative methods. This paper surveys the published research and the proposed solutions. It is concluded that there remains a need for further research with a focus on real world applicability of a method or methods to address the digital forensic data volume challenge.     

The ICO and artificial intelligence: The role of fairness in the GDPR framework

The year 2017 has seen many EU and UK legislative initiatives and proposals to consider and address the impact of artificial intelligence on society, covering questions of liability, legal personality and other ethical and legal issues, including in the context of data processing. In March 2017, the Information Commissioner's Office (UK) updated its big data guidance to address the development of artificial intelligence and machine learning, and to provide (GDPR), which will apply from 25 May 2018.This paper situates the ICO's guidance in the context of wider legal and ethical considerations and provides a critique of the position adopted by the ICO. On the ICO's analysis, the key challenge for artificial intelligence processing personal data is in establishing that such processing is fair. This shift reflects the potential for artificial intelligence to have negative social consequences (whether intended or unintended) that are not otherwise addressed by the GDPR. The question of ‘fairness’ is an important one, to address the imbalance between big data organisations and individual data subjects, with a number of ethical and social impacts that need to be evaluated.     

Preventing discrimination in the automated targeting of job advertisements

On the background of the increasing amount of discriminatory challenges facing artificial intelligence applications, this paper examines the requirements that are needed to comply with European non-discrimination law to prevent discrimination in the automated online job advertising business. This paper explains under which circumstance the automated targeting of job advertisements can amount to direct or indirect discrimination. The paper concludes with technical recommendations to dismantle the dangers of automated job advertising. Various options like influencing the pre-processing of big data and altering the algorithmic models are evaluated. This paper also examines the possibilities of using techniques like data mining and machine learning to actively battle direct and indirect discrimination. The European non-discrimination directives 2000/43/EC, 2000/78/EC, and 2006/54/EC which prohibit direct and indirect discrimination in the field of employment on the grounds of race or ethnic origin, sex, sexual orientation, religious belief, age and disability are used as a legal framework.