**1. Introduction**

The production and consumption of data has been increasing with the ubiquity of the internet [1], and with this, the benefits that accompany innovations and advances in computing technology, such as those stemming from artificial intelligence and machine learning, are increasingly relevant to a growing number of industries and applications [2]. However, our reliance on technology and consumer connectedness, coupled with rapid growth in the aggregation and liquidity of personalized data, has made us more vulnerable to cybercrime victimization and the malicious use of private data [3, 4]. The challenge of securing confidential information is becoming one of the key issues in our digital world [5].

Recently developed privacy-enhancing technologies and methods are being touted as possible solutions to mitigate privacy risks associated with inadvertent disclosure and guard against sinister data incursions resulting from cybercrime. One such possibility is Differential Privacy [6], which represents a new security paradigm designed to meet the growing number of privacy risks which accompany data stewardship, particularly for those entrusted with safeguarding data. Differential Privacy was conceived to simultaneously harness the power of information contained in "big data" while substantially reducing the likelihood of harmful data disclosures resulting in possible malicious use [7].

The commercial benefits and costs of privacy enhancing technologies have been widely studied, particularly as consumer data sharing and consumption has grown through distributed systems and Internet of Things (IOT) devices and applications such as smartphones, televisions, medical equipment, appliances, and wearables. However, because of its emergence as a promising new approach to computational analysis, far less has been written about the implications of Differential Privacy, including the merits and limitations of the sophisticated techniques created in the context of this definition. Similarly, research aimed at the advantages, pitfalls, and practical challenges of adopting differentially private approaches has been limited. Literature on Differential Privacy has yet to explore the applied use of this privacypreserving approach in the context of contemporary crime and justice threats, including cybercrime. Scholarship has generally tended to avoid important, and arguably necessary, cross-disciplinary collaborations between technical science disciplines such as computer science and social science disciplines like criminal justice.

Therefore, through the lens of the criminal justice discipline, this chapter will explore the use of Differential Privacy as a possible cybercrime prevention technique in the context of the massive digital ecosystem that has emerged over the last two decades. We begin with a discussion of the recent proliferation of cybercrime that has arisen through advances in technology, followed by a brief examination of evolving privacy protections which led to the rise of differential privacy, as both a general tenet and assortment of techniques for advancing data security. We then speculate on the use of Differential Privacy as a situational crime prevention countermeasure to cybercrime, and review potential challenges to its use. The chapter concludes with an attempt to stimulate future research and interest in cross-disciplinary exploration of this relatively new privacy-enhancing approach, particularly with respect to its potential to reduce risk, combat crime, and preserve the confidentiality of data for consumers and those most vulnerable to cybercrime victimization.
