  Breached data refers to the unauthorized access, theft, or exposure of
confidential or sensitive information. Breaches typically occur when malicious
actors or unauthorized users breach secure systems or networks, resulting in
compromised personally identifiable information (PII), protected or personal
health information (PHI), payment card industry (PCI) information, or other
sensitive data. Data breaches are often the result of malicious activities such
as hacking, phishing, insider threats, malware, or physical theft. The misuse
of breached data can lead to identity theft, fraud, spamming, or blackmailing.
Organizations that experience data breaches may face legal and financial
consequences, reputational damage, and harm to their customers or users.
Breached records are commonly sold on the dark web or made available on various
public forums. To counteract these malicious activities, it is possible to
collect breached databases and mitigate potential harm. These databases can be
quite large, reaching sizes of up to 150 GB or more. Typically, breached data
is stored in the CSV (Comma Separated Value) format due to its simplicity and
lightweight nature, which reduces storage requirements. Analyzing and
traversing large breached databases necessitates substantial computational
power. However, this research explores techniques to optimize database
traversal speed without the need to rent expensive cloud machines or virtual
private servers (VPS). This optimization will enable individual security
researchers to analyze and process large databases on their personal computer
systems while significantly reducing costs.
