Visible to the public Data Breach and Multiple Points to Stop It

TitleData Breach and Multiple Points to Stop It
Publication TypeConference Paper
Year of Publication2018
AuthorsYao, Danfeng(Daphne)
Conference NameProceedings of the 23Nd ACM on Symposium on Access Control Models and Technologies
Conference LocationNew York, NY, USA
ISBN Number978-1-4503-5666-4
KeywordsAccess Control, anomaly detection, Data Breach, data loss prevention, Human Behavior, inadvertent data leak, insider threat, Metrics, multiple point of prevention, pubcrawl, ransomware, resilience, security practices, software security, system security
AbstractPreventing unauthorized access to sensitive data is an exceedingly complex access control problem. In this keynote, I will break down the data breach problem and give insights into how organizations could and should do to reduce their risks. The talk will start with discussing the technical reasons behind some of the recent high-profile data breach incidents (e.g., in Equifax, Target), as well as pointing out the threats of inadvertent or accidental data leaks. Then, I will show that there are usually multiple points to stop data breach and give an overview of the relevant state-of-the-art solutions. I will focus on some of the recent algorithmic advances in preventing inadvertent data loss, including set-based and alignment-based screening techniques, outsourced screening, and GPU-based performance acceleration. I will also briefly discuss the role of non-technical factors (e.g., organizational culture on security) in data protection. Because of the cat-and-mouse-game nature of cybersecurity, achieving absolute data security is impossible. However, proactively securing critical data paths through strategic planning and placement of security tools will help reduce the risks. I will also point out a few exciting future research directions, e.g., on data leak detection as a cloud security service and deep learning for reducing false alarms in continuous authentication and the prickly insider-threat detection.
Citation Keyyao_data_2018