The largest reported data leakage as of January 2024 was the Cam4 data breach in March 2020, which exposed more than 10 billion data records. The second-largest data breach in history so far, the Yahoo data breach, occurred in 2013. The company initially reported about one billion exposed data records, but after an investigation, the company updated the number, revealing that three billion accounts were affected. The National Public Data Breach was announced in August 2024. The incident became public when personally identifiable information of individuals became available for sale on the dark web. Overall, the security professionals estimate the leakage of nearly three billion personal records. The next significant data leakage was the March 2018 security breach of India's national ID database, Aadhaar, with over 1.1 billion records exposed. This included biometric information such as identification numbers and fingerprint scans, which could be used to open bank accounts and receive financial aid, among other government services.

Cybercrime - the dark side of digitalization As the world continues its journey into the digital age, corporations and governments across the globe have been increasing their reliance on technology to collect, analyze and store personal data. This, in turn, has led to a rise in the number of cyber crimes, ranging from minor breaches to global-scale attacks impacting billions of users – such as in the case of Yahoo. Within the U.S. alone, 1802 cases of data compromise were reported in 2022. This was a marked increase from the 447 cases reported a decade prior. The high price of data protection As of 2022, the average cost of a single data breach across all industries worldwide stood at around 4.35 million U.S. dollars. This was found to be most costly in the healthcare sector, with each leak reported to have cost the affected party a hefty 10.1 million U.S. dollars. The financial segment followed closely behind. Here, each breach resulted in a loss of approximately 6 million U.S. dollars - 1.5 million more than the global average.

In 2023, the number of data compromises in the United States stood at 3,205 cases. Meanwhile, over 353 million individuals were affected in the same year by data compromises, including data breaches, leakage, and exposure. While these are three different events, they have one thing in common. As a result of all three incidents, the sensitive data is accessed by an unauthorized threat actor. Industries most vulnerable to data breaches Some industry sectors usually see more significant cases of private data violations than others. This is determined by the type and volume of the personal information organizations of these sectors store. In 2022, healthcare, financial services, and manufacturing were the three industry sectors that recorded most data breaches. The number of healthcare data breaches in the United States has gradually increased within the past few years. In the financial sector, data compromises increased almost twice between 2020 and 2022, while manufacturing saw an increase of more than three times in data compromise incidents. Largest data exposures worldwide In 2020, an adult streaming website, CAM4, experienced a leakage of nearly 11 billion records. This, by far, is the most extensive reported data leakage. This case, though, is unique because cyber security researchers found the vulnerability before the cyber criminals. The second-largest data breach is the Yahoo data breach, dating back to 2013. The company first reported about one billion exposed records, then later, in 2017, came up with an updated number of leaked records, which was three billion. In March 2018, the third biggest data breach happened, involving India’s national identification database Aadhaar. As a result of this incident, over 1.1 billion records were exposed.

Between 2004 and October 2024, the United States recorded the highest number of data points leaked online. Overall, more than 17 billion data points were leaked in the country during the measured period. Russia ranked second, with more than four billion leaked data points.

Analysis of ‘List of Top Data Breaches (2004 - 2021)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/hishaamarmghan/list-of-top-data-breaches-2004-2021 on 14 February 2022.

--- Dataset description provided by original source is as follows ---

This is a dataset containing all the major data breaches in the world from 2004 to 2021

As we know, there is a big issue related to the privacy of our data. Many major companies in the world still to this day face this issue every single day. Even with a great team of people working on their security, many still suffer. In order to tackle this situation, it is only right that we must study this issue in great depth and therefore I pulled this data from Wikipedia to conduct data analysis. I would encourage others to take a look at this as well and find as many insights as possible.

This data contains 5 columns: 1. Entity: The name of the company, organization or institute 2. Year: In what year did the data breach took place 3. Records: How many records were compromised (can include information like email, passwords etc.) 4. Organization type: Which sector does the organization belong to 5. Method: Was it hacked? Were the files lost? Was it an inside job?

Here is the source for the dataset: https://en.wikipedia.org/wiki/List_of_data_breaches

Here is the GitHub link for a guide on how it was scraped: https://github.com/hishaamarmghan/Data-Breaches-Scraping-Cleaning

--- Original source retains full ownership of the source dataset ---

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Replication Data and Code for "Incentives and Information in Methane Leak Detection and Repair" Abstract: Capturing leaked methane can be a win for both firms and the environment. However, leakage volume uncertainty can be a barrier inhibiting leak repair. We study an experiment at oil and gas production sites which randomized whether site operators were informed of methane leakage volumes. At sites with high baseline leakage, we estimate a negative but imprecise effect of information on endline emissions. But at sites with zero measured leakage, giving firms information about methane leakage increased emissions at endline. Our results suggest that giving firms news of low leakage disincentivizes maintenance effort, thereby increasing the likelihood of future leaks. Package includes data from Wang et al. (2024) RCT as well as IEA data on estimated methane emissions and methane abatement costs. Package also includes code for replication.

About The Nauru Files contain the largest set of documents published from inside Australia's immigration detention system. Leaked to The Guardian in 2016, they include nearly 2,000 incident reports from the Nauru detention centre, which were written by guards, caseworkers and teachers on the remote Pacific island. Summary Examples of events include assaults, injuries, abuse and other forms of violence reported at the detention centre between 2013 and 2015. As noted by The Guardian, as well as academic research, Australia has privatised its immigration detention centres and exported detention of asylum seekers offshore to places such as Nauru and Manus Island in Papua New Guinea. This strategy is part of a wider "Pacific Solution" implemented by the Government of Australia since the early 2000s as a hardline deterrent to "stop the boats." Effectively, asylum seekers intercepted and detained on Nauru are removed from access to Australia's asylum system. Data Structure These data are composed of incident reports. An incident report is a short summary of an event in the Nauru detention centre written by staff there. Some of the details found in the files may be triggering; we therefore advise caution with reading and analysing these data. According to The Guardian, these reports form part of the Government of Australia's requirements to document what is happening within its detention system. Each report holds detailed information of the incident at the detention centre along with a "summary log". Working with The Guardian, we have organised these data into two forms: a PDF of each incident report, sorted by name at the time of leak, and a CSV/JSON of all incident reports (see "nauru_files.csv/json"), which structures key details into variables within its columns. Examples of variables include time, incident type, severity and description. Combined, these form a structured database linking each incident report to these variables. Data Source The Guardian has modified the original, leaked data to remove any personally-identifying information within them. To achieve this, a stringent approach of redaction has been implemented to remove names of asylum seekers and staff, personal identification numbers of asylum seekers, signatures of detention staff, nationalities within small population groups and residential tent numbers, among other things. There are also a large number of acronyms used in these data. For your convenience, we have provided an RTF document with a listing of these acronyms and their meanings. If you use these data, please cite the original source at The Guardian: The Guardian. (10 August 2016). The Nauru Files: The lives of asylum seekers in detention detailed in a unique database. Retrieved from https://www.theguardian.com/australia-news/ng-interactive/2016/aug/10/the-nauru-files-the-lives-of-asylum-seekers-in-detention-detailed-in-a-unique-database-interactive. Should you have any comments, questions or requested edits or extensions to the Nauru files, please contact Haven at kira.williams@utoronto.ca. For more articles from The Guardian on these data, see: The Nauru files: cache of 2,000 leaked reports reveal scale of abuse of children in Australian offshore detention. A short history of Nauru, Australia’s dumping ground for refugees. ‘I want death’: Nauru files chronicle despair of asylum seeker children.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

The U.S. outer continental shelf is a major source of energy for the United States. The rapid growth of oil and gas production in the Gulf of Mexico increases the risk of underwater oil spills at greater water depths and drilling wells. These hydrocarbons leakages can be caused by either natural events, such as seeping from fissures in the ocean seabed, or by anthropogenic accidents, such as leaking from broken wellheads and pipelines. In order to improve safety and reduce the environmental risks of offshore oil and gas operations, the Bureau of Safety and Environmental Enforcement (BSEE) recommended the use of real-time monitoring. An early warning system for detecting, locating, and characterizing hydrocarbon leakages is essential for preventing the next oil spill as well as for seafloor hydrocarbon seepage detection. Existing monitoring techniques have significant limitations and cannot achieve real-time monitoring. This project launches an effort to develop a functional real-time monitoring system that uses passive acoustic technologies to detect, locate, and characterize undersea hydrocarbon leakages over large areas in a cost-effective manner. In an oil spill event, the leaked hydrocarbon is injected into seawater with huge amounts of discharge at high speeds. With mixed natural gases and oils, this hydrocarbon leakage creates underwater sound through two major mechanisms: shearing and turbulence by a streaming jet of oil droplets and gas bubbles, and bubble oscillation and collapse. These acoustic emissions can be recorded by hydrophones in the water column at far distances. They will be characterized and differentiated from other underwater noises through their unique frequency spectrum, evolution and transportation processes and leaking positions, and further, be utilized to detect and position the leakage locations. With the objective of leakage detection and localization, our approach consisted of recording and modeling the acoustic signals induced by the oil spill and implementing advanced signal processing and triangulation localization techniques with a hydrophone network. Tasks of this project were: 1. Conduct a laboratory study to simulate hydrocarbon leakages and their induced sound under controlled conditions, and to establish the correlation between frequency spectra and leakage properties, such as oil-jet intensities and speeds, bubble radii and distributions, and crack sizes. 2. Implement and develop acoustic bubble modeling for estimating features and strength of the oil leakage. 3. Develop a set of advanced signal processing and triangulation algorithms for leakage detection and localization. The experimental data have been collected in a water tank in the building of the National Center for Physical Acoustics, the University of Mississippi from 2018-2020, including hydrophone recorded underwater sounds generated by oil leakage bubbles under different testing conditions, such as pressures, flow rates, jet velocities, and crack sizes, and movies of oil leakages. Two types of oil leakages (a few bubbles and constant flow bubbles) were tested to simulate oil seepages either from seafloors or from oil well and pipeline breaches. Two types of gases were investigated (nitrogen and methane). These data were analyzed for acoustic bubble modeling, oil leakage characterization, and localization. This dataset contains data for oil leakage source localization. Two localization algorithms were developed: TDOA-based and SpectraRatio-based algorithms. The folders of the dataset are described as follows: • the folders of “Signals” contain raw underwater sounds data used for localization • the folders of “Results” contain the results of true and predicted oil leakage source positions More details of this dataset can be found in the corresponding ReadMe files in each folder.

The U.S. outer continental shelf is a major source of energy for the United States. The rapid growth of oil and gas production in the Gulf of Mexico increases the risk of underwater oil spills at greater water depths and drilling wells. These hydrocarbons leakages can be caused by either natural events, such as seeping from fissures in the ocean seabed, or by anthropogenic accidents, such as leaking from broken wellheads and pipelines. In order to improve safety and reduce the environmental risks of offshore oil and gas operations, the Bureau of Safety and Environmental Enforcement recommended the use of real-time monitoring. An early warning system for detecting, locating, and characterizing hydrocarbon leakages is essential for preventing the next oil spill as well as for seafloor hydrocarbon seepage detection. Existing monitoring techniques have significant limitations and cannot achieve real-time monitoring. This project launches an effort to develop a functional real-time monitoring system that uses passive acoustic technologies to detect, locate, and characterize undersea hydrocarbon leakages over large areas in a cost-effective manner.

In an oil spill event, the leaked hydrocarbon is injected into seawater with huge amounts of discharge at high speeds. With mixed natural gases and oils, this hydrocarbon leakage creates underwater sound through two major mechanisms: shearing and turbulence by a streaming jet of oil droplets and gas bubbles, and bubble oscillation and collapse. These acoustic emissions can be recorded by hydrophones in the water column at far distances. They will be characterized and differentiated from other underwater noises through their unique frequency spectrum, evolution and transportation processes and leaking positions, and further be utilized to detect and position the leakage locations.

With the objective of leakage detection and localization, our approach consists of recording and modeling the acoustic signals induced by the oil-spill and implementing advanced signal processing and triangulation localization techniques with a hydrophone network.

Tasks of this project are: 1. Conduct a laboratory study to simulate hydrocarbon leakages and their induced sound under controlled conditions, and to establish the correlation between frequency spectra and leakage properties, such as oil-jet intensities and speeds, bubble radii and distributions, and crack sizes. 2. Implement and develop acoustic bubble modeling for estimating features and strength of the oil leakage. 3. Develop a set of advanced signal processing and triangulation algorithms for leakage detection and localization.

The experimental data have been collected in a water tank in the building of the National Center for Physical Acoustics, the University of Mississippi from 2018-2020, including hydrophone recorded underwater sounds generated by oil leakage bubbles under different testing conditions, such as pressures, flow rates, jet velocities, and crack sizes, and movies of oil leakages. Two types of oil leakages (a few bubbles and constant flow bubbles) were tested to simulate oil seepages either from seafloors or from oil well and pipe-line breaches. Two types of gases were investigated (nitrogen and methane). These data were analyzed for acoustic bubble modeling, oil leakage characterization, and localization.

This dataset contains programs and algorithms. The folders of the dataset are described as follows: • the folder of “signal processing programs†contains programs (LabView VIs) for instrument control, data acquisition, and signal processing. • the folders of “modeling algorithms†contains algorithms (Matlab m-files) for acoustic bubble sound modeling. • the folder of “localization algorithms†contains algorithms (MatLab m-files) for oil leakage source localization.

More details of this dataset can be found in the corresponding ReadMe files in each folder. Associated data may be found in S3.x911.000:0001 (bubble sound characterization and modeling data, doi:10.7266/3REPB7QM); S3.x911.000:0002 (test data, doi: 10.7266/NPYZ3XFV); S3.x911.000:0003 (raw sound data and validation of modeled source positions, doi: 10.7266/4S9EBZKX); S3.x911.000:0005 (imagery of the laboratory experiment, doi: 10.7266/BZY62EK0).

The U.S. outer continental shelf is a major source of energy for the United States. The rapid growth of oil and gas production in the Gulf of Mexico increases the risk of underwater oil spills at greater water depths and drilling wells. These hydrocarbons leakages can be caused by either natural events, such as seeping from fissures in the ocean seabed, or by anthropogenic accidents, such as leaking from broken wellheads and pipelines. In order to improve safety and reduce the environmental risks of offshore oil and gas operations, the Bureau of Safety and Environmental Enforcement recommended the use of real-time monitoring. An early warning system for detecting, locating, and characterizing hydrocarbon leakages is essential for preventing the next oil spill as well as for seafloor hydrocarbon seepage detection. Existing monitoring techniques have significant limitations and cannot achieve real-time monitoring. This project launches an effort to develop a functional real-time monitoring system that uses passive acoustic technologies to detect, locate, and characterize undersea hydrocarbon leakages over large areas in a cost-effective manner. In an oil spill event, the leaked hydrocarbon is injected into seawater with huge amounts of discharge at high speeds. With mixed natural gases and oils, this hydrocarbon leakage creates underwater sound through two major mechanisms: shearing and turbulence by a streaming jet of oil droplets and gas bubbles, and bubble oscillation and collapse. These acoustic emissions can be recorded by hydrophones in the water column at far distances. They will be characterized and differentiated from other underwater noises through their unique frequency spectrum, evolution and transportation processes and leaking positions, and further be utilized to detect and position the leakage locations. With the objective of leakage detection and localization, our approach consists of recording and modeling the acoustic signals induced by the oil-spill and implementing advanced signal processing and triangulation localization techniques with a hydrophone network. Tasks of this project were: 1. Conduct a laboratory study to simulate hydrocarbon leakages and their induced sound under controlled conditions, and to establish the correlation between frequency spectra and leakage properties, such as oil-jet intensities and speeds, bubble radii and distributions, and crack sizes. 2. Implement and develop acoustic bubble modeling for estimating features and strength of the oil leakage. 3. Develop a set of advanced signal processing and triangulation algorithms for leakage detection and localization. The experimental data have been collected in a water tank in the building of the National Center for Physical Acoustics, the University of Mississippi from 2018-2020, including hydrophone recorded underwater sounds generated by oil leakage bubbles under different testing conditions, such as pressures, flow rates, jet velocities, and crack sizes, and movies of oil leakages. Two types of oil leakages (a few bubbles and constant flow bubbles) were tested to simulate oil seepages either from seafloors or from oil well and pipe-line breaches. Two types of gases were investigated (nitrogen and methane). These data were analyzed for acoustic bubble modeling, oil leakage characterization, and localization. This dataset contains data for acoustic bubble sound modeling of nitrogen and methane, using a range of jet sizes and flow rates. The data for oil leakage source localization is available under GRIIDC Unique Dataset Identifier (UDI): S3.x911.000:0003 (DOI: 10.7266/4S9EBZKX).