The largest reported data leakage as of January 2025 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 2024, the number of data compromises in the United States stood at 3,158 cases. Meanwhile, over 1.35 billion 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 2024 the financial services, healthcare, and professional services were the three industry sectors that recorded most data breaches. Overall, the number of healthcare data breaches in some industry sectors in the United States has gradually increased within the past few years. However, some sectors saw decrease. 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.

The data center water leak detector market is experiencing robust growth, driven by the increasing adoption of data centers globally and the rising awareness of the significant financial and operational losses associated with water damage. The market, estimated at $500 million in 2025, is projected to achieve a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This growth is fueled by several key factors: the escalating demand for high-availability and uptime in data centers, stricter regulatory compliance requirements regarding data center safety, and the continuous advancement of leak detection technologies offering greater precision and faster response times. The market is segmented by application (commercial, industrial, other) and type (non-positioned and positioned leak detection). The positioned water leak detection segment is expected to dominate due to its ability to pinpoint leaks precisely, minimizing downtime and repair costs. North America and Europe currently hold the largest market share, driven by high data center density and strong regulatory frameworks. However, the Asia-Pacific region is poised for significant growth, fueled by rapid data center construction in countries like China and India. Market restraints include the high initial investment cost of deploying advanced leak detection systems, particularly in smaller data centers. However, the long-term cost savings associated with preventing catastrophic water damage significantly outweigh this initial investment. Furthermore, the increasing availability of cloud-based monitoring and remote management solutions is further driving adoption by streamlining maintenance and reducing operational overhead. Leading companies in this market are actively innovating to enhance the functionality and cost-effectiveness of their products, including integrating advanced analytics and AI for predictive maintenance and proactive leak prevention. The competitive landscape is characterized by a mix of established players and emerging technology providers, driving innovation and creating opportunities for market consolidation in the coming years.

Context

The Paradise Papers is a cache of some 13GB of data that contains 13.4 million confidential records of offshore investment by 120,000 people and companies in 19 tax jurisdictions (Tax Heavens - an awesome video to understand this); that was published by the International Consortium of Investigative Journalists (ICIJ) on November 5, 2017. Here is a brief video about the leak. The people include Queen Elizabeth II, the President of Columbia (Juan Manuel Santos), Former Prime Minister of Pakistan (Shaukat Aziz), U.S Secretary of Commerce (Wilbur Ross) and many more. According to an estimate by the Boston Consulting Group, the amount of money involved is around $10 trillion. The leak contains many famous companies, including Facebook, Apple, Uber, Nike, Walmart, Allianz, Siemens, McDonald’s and Yahoo.

It also contains a lot of U. S President Donald Trump allies including Rax Tillerson, Wilbur Ross, Koch Brothers, Paul Singer, Sheldon Adelson, Stephen Schwarzman, Thomas Barrack and Steve Wynn etc. The complete list of Politicians involve is avaiable here.

The Panama Papers in the cache of 38GB of data from the national corporate registry of Bahamas. It contains world’s top politicians and influential persons as head and director of offshore companies registered in Bahamas.

Offshore Leaks details 13,000 offshore accounts in a report.

I am calling all data scientists to help me stop the corruption and reveal the patterns and linkages invisible for the untrained eye.

Content

The data is the effort of more than 100 journalists from 60+ countries

The original data is available under creative common license and can be downloaded from this link.

I will keep updating the datasets with more leaks and data as it’s available

Acknowledgements

International Consortium of Investigative Journalists (ICIJ)

Paradise Papers Update

Paradise Papers data has been uploaded as released by ICIJ on Nov 21, 2017. You can find Paradise Papers zip file and six extracted files in CSV format, all starting with a prefix of Paradise. Happy Coding!

Inspiration

Some ideas worth exploring:

1. How many companies and individuals are there in all of the leaks data

2. How many countries involved

3. Total money involved

4. What is the biggest best tax heaven

5. Can we compare the corruption with human development index and make an argument that would correlate corruption with bad conditions in that country

6. Who are the biggest cheaters and where they live

7. What role Fortune 500 companies play in this game

I need your help to make this world corruption free in the age of NLP and Big Data

Future energy systems may rely on natural gas as a low-cost fuel to support variable renewable power. However, leaking natural gas causes climate damage because methane (CH4) has a high global warming potential. In this study, we use extreme-value theory to explore the distribution of natural gas leak sizes. By analyzing ∼15 000 measurements from 18 prior studies, we show that all available natural gas leakage data sets are statistically heavy-tailed, and that gas leaks are more extremely distributed than other natural and social phenomena. A unifying result is that the largest 5% of leaks typically contribute over 50% of the total leakage volume. While prior studies used log-normal model distributions, we show that log-normal functions poorly represent tail behavior. Our results suggest that published uncertainty ranges of CH4 emissions are too narrow, and that larger sample sizes are required in future studies to achieve targeted confidence intervals. Additionally, we find that cross-study aggregation of data sets to increase sample size is not recommended due to apparent deviation between sampled populations. Understanding the nature of leak distributions can improve emission estimates, better illustrate their uncertainty, allow prioritization of source categories, and improve sampling design. Also, these data can be used for more effective design of leak detection technologies.

Automatic leak localization has been suggested to reduce the time and personnel efforts needed to localize (small) leaks. Yet, the available methods require a detailed demand distribution model for successful calibration and good leak localization performance. The main aim of this work was to analyze whether such a detailed demand distribution is needed. Two demand distributions were used: a factorized distribution that distributes the inflow demand proportionally across the consumption nodes according to individual billing data, and a uniform distribution that equally distributes demand across all consumption nodes. The performance of the automatic leak localization method, using both demand distribution models, was compared. A new measure for leak localization performance that is based on the percentage of false positive nodes is proposed. It was possible to localize the leaks with both demand distribution models, although performance varied depending on the timing and duration of the measurement.

Germany Water Losses: Leakage data was reported at 473.172 Cub m mn in 2019. This records an increase from the previous number of 456.453 Cub m mn for 2016. Germany Water Losses: Leakage data is updated yearly, averaging 470.614 Cub m mn from Dec 2007 (Median) to 2019, with 5 observations. The data reached an all-time high of 474.000 Cub m mn in 2010 and a record low of 456.453 Cub m mn in 2016. Germany Water Losses: Leakage data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Germany – Table DE.OECD.ESG: Environmental: Water Made Available for Use: OECD Member: Annual.

This dataset was created as a compilation of experimental data in the literature on the production of medium chain carboxylic acids (MCCAs) by microbial mixed cultures (MMC) fermentation. The intention was to provide a dataset as comprehensive as possible that includes the majority of experimental results available in this research area to the best of our knowledge. The focus lied on MMC-based studies processing complex organic feedstock, yet selected studies were included on synthetic substrates. The relevant literature studies were collected and experimental results categorized according to bioreactor operation, i.e. batch, fed-batch and (semi-)continuous. Operational parameters, such as feedstock type, organic loading rate, temperature, etc., were extracted from information reported in studies and placed alongside product outcome in terms of MCCA production for each experiment. This dataset forms the backbone of the discussion and figure generation of the literature review "Medium chain carboxylic acids from complex organic feedstock by mixed culture fermentation" by V. De Groof, M. Coma, T. Arnot, D. Leak, A. Lanham. Published in MDPI Molecules: Special Issue "Chemicals from Food Supply Chain By-Products and Waste Streams" 2019.

Full title: Using Decision Trees to Detect and Isolate Simulated Leaks in the J-2X Rocket Engine

Mark Schwabacher, NASA Ames Research Center

Robert Aguilar, Pratt & Whitney Rocketdyne

Fernando Figueroa, NASA Stennis Space Center

Abstract

The goal of this work was to use data-driven methods to automatically detect and isolate faults in the J-2X rocket engine. It was decided to use decision trees, since they tend to be easier to interpret than other data-driven methods. The decision tree algorithm automatically “learns” a decision tree by performing a search through the space of possible decision trees to find one that fits the training data. The particular decision tree algorithm used is known as C4.5. Simulated J-2X data from a high-fidelity simulator developed at Pratt & Whitney Rocketdyne and known as the Detailed Real-Time Model (DRTM) was used to “train” and test the decision tree. Fifty-six DRTM simulations were performed for this purpose, with different leak sizes, different leak locations, and different times of leak onset. To make the simulations as realistic as possible, they included simulated sensor noise, and included a gradual degradation in both fuel and oxidizer turbine efficiency. A decision tree was trained using 11 of these simulations, and tested using the remaining 45 simulations. In the training phase, the C4.5 algorithm was provided with labeled examples of data from nominal operation and data including leaks in each leak location. From the data, it “learned” a decision tree that can classify unseen data as having no leak or having a leak in one of the five leak locations. In the test phase, the decision tree produced very low false alarm rates and low missed detection rates on the unseen data. It had very good fault isolation rates for three of the five simulated leak locations, but it tended to confuse the remaining two locations, perhaps because a large leak at one of these two locations can look very similar to a small leak at the other location.

Introduction

The J-2X rocket engine will be tested on Test Stand A-1 at NASA Stennis Space Center (SSC) in Mississippi. A team including people from SSC, NASA Ames Research Center (ARC), and Pratt & Whitney Rocketdyne (PWR) is developing a prototype end-to-end integrated systems health management (ISHM) system that will be used to monitor the test stand and the engine while the engine is on the test stand[1]. The prototype will use several different methods for detecting and diagnosing faults in the test stand and the engine, including rule-based, model-based, and data-driven approaches. SSC is currently using the G2 tool http://www.gensym.com to develop rule-based and model-based fault detection and diagnosis capabilities for the A-1 test stand. This paper describes preliminary results in applying the data-driven approach to detecting and diagnosing faults in the J-2X engine. The conventional approach to detecting and diagnosing faults in complex engineered systems such as rocket engines and test stands is to use large numbers of human experts. Test controllers watch the data in near-real time during each engine test. Engineers study the data after each test. These experts are aided by limit checks that signal when a particular variable goes outside of a predetermined range. The conventional approach is very labor intensive. Also, humans may not be able to recognize faults that involve the relationships among large numbers of variables. Further, some potential faults could happen too quickly for humans to detect them and react before they become catastrophic. Automated fault detection and diagnosis is therefore needed. One approach to automation is to encode human knowledge into rules or models. Another approach is use data-driven methods to automatically learn models from historical data or simulated data. Our prototype will combine the data-driven approach with the model-based and rule-based approaches. This paper focuses on the data-driven approach.

The J-2X Engine

The J-2X is a rocket engine currently under development at Pratt & Whitney Rocketdyne. It will be fueled by liquid hydrogen and liquid oxygen. It will be used as the second-stage engine on NASA’s Ares I crew launch vehicle http://www.nasa.gov/mission_pages/constellation/ares/aresl/ and Ares V cargo launch vehicle http://www.nasa.gov/mission_pages/constellation/ares/aresV/. It is derived from the J-2 engine, which served as the second- and third-stage engines on the Saturn V launch vehicle. The J-2X engine is shown in Figure 1. http://www.pw.utc.com/vgn-ext-templating/v/index.jsp?vgnextrefresh=1&vgnextoid=8fd0586642738110VgnVCM100000c45a529fRCRD

Test Stand A-1

SSC operates several rocket engine test stands. Each test stand provides a structure strong enough to hold a rocket engine in place as it is fired, and a fuel feed system to provide fuel to the engine. Test stand A-1 is a large test stand that is currently used to test the space shuttle's main engines, and will be used to test the J-2X. It can withstand a maximum dynamic load of 1.7 million pounds of force. It provides liquid hydrogen and liquid oxygen to the engine being tested, and has numerous sensors on its fuel feed system. Test Stand A-1 is shown in Figure 2. http://rockettest.nasa.gov/rptmb/ssc_a1_test_stand.asp

The J-2X Detailed Real-Time Model

We used data from a high-fidelity physics-based simulator to train and test the data-driven algorithms. The physics-based model chosen for this project is the J-2X Detailed Transient Model or DTM. The J-2X DTM, as the name indicates, is a transient model that accurately models all phases of engine operation including start, mainstage (phase between start and shutdown), and shutdown. The J-2X DTM simulates processes describing rocket engine operation including heat transfer, fluid flow, combustion and valve dynamics. Flowrates, pump speeds, temperatures and pressures are modeled as time dependent differential equations that are updated at a high rate, typically 2000 Hz. Property tables, valve characteristics and turbomachinery efficiency and performance curves are also incorporated in the DTM. DTM’s are used to develop safe start and shutdown sequences and for anomaly resolution. The J-2X DTM builds on a long history of DTM’s supporting most major Pratt & Whitney Rocketdyne (PWR) rocket engines. The J-2X DTM underwent modification to enable it to run in “real-time” mode. In real-time mode, the DTM will respond in real world clock time to external stimuli such as changes in valve position and engine inlet conditions. The latter will comprise the interface to the test stand model. Advances in computer processor technology have made this possible due to the fast update rate required to maintain numeric stability. (Faster update rates imply smaller time steps, which result in smaller errors, which result in greater stability.) Real-time performance is achieved if a model advances in time (step time) at the same rate as a wall clock. If a processor can perform all calculations in a step time or less, then the model is real-time capable. The step time should also be consistent and set to the longest measured step time corresponding to the longest logical path. Shorter frames are then padded to provide a deterministic step time. The J-2X DTM, or any DTM for that matter, was not optimized for real-time operation. Changes that were required include streamlining model code, limiting or eliminating model diagnostic output, and fixing the step time. The J-2X DTM currently uses a variable step time to maintain numeric stability so deterministic timing is not possible. Real-time DTM operation is required when communication to other real-time components of a system is required such as hardware-in-the-loop testing or for online monitoring of an engine and test stand. Near real-time operation has been demonstrated indicating full real-time operation is feasible in the near future. The modified DTM now has the designation J-2X Detailed Real-Time Model or DRTM. The DRTM was modified to enable failure mode simulation. Failure modes are modeled as changes to the flowpath of the DRTM (e.g. leaks) or modification of engine parameters (e.g. turbine efficiency) representative of failure signatures. Sensor characteristics, such as lag and bit toggle, and process noise were also modeled to better replicate engine operation. A simulation of cavitation due to low inlet pressure was also added to the DRTM as the primary test stand/engine interface fault mode. As the inlet pressure falls below a certain level, the propellant begins to vaporize and pump performance drops dramatically.

Data-driven fault detection and diagnostics

In our previous work[2,3] , we used unsupervised anomaly detection algorithms to automatically detect faults in Space Shuttle Main Engine data. Unsupervised anomaly detection algorithms are trained using only nominal data. They learn a model of the nominal data, and signal an anomaly when new data fails to match the model. They are useful when few examples of failure data are available. For a rocket such as the Space Shuttle Main Engine, very few examples of failures exist in the historical data. Unsupervised anomaly detection algorithms are therefore useful when using historical data as training data. For the J-2X, no real data is available yet, since the engine has not been built yet. However, we do have a high-fidelity physics-based simulator that can simulate faults. We therefore decided to use supervised learning. When used for fault detection and

• The global industry was valued at US$ 4.0 Bn in 2023
• It is estimated to grow at a CAGR of 6.1% from 2024 to 2034 and reach US$ 7.8 Bn by the end of 2034

Market Introduction

Attribute	Detail
Drivers	Surge in Focus on Limiting Greenhouse Gas Emissions R&D of New Products

Regional Outlook

Attribute	Detail
Leading Region	Asia Pacific

Drone-based Gas Leak Detection in Oil & Gas Market Snapshot

Attribute	Detail
Market Size in 2023	US$ 4.0 Bn
Market Forecast (Value) in 2034	US$ 7.8 Bn
Growth Rate (CAGR)	6.1%
Forecast Period	2024-2034
Historical Data Available for	2020-2022
Quantitative Units	US$ Bn for Value
Market Analysis	It includes segment analysis as well as regional level analysis. Furthermore, qualitative analysis includes drivers, restraints, opportunities, key trends, Porter’s Five Forces Analysis, value chain analysis, and key trend analysis.
Competition Landscape	Market share analysis by company (2023) Company profiles section includes overview, product portfolio, sales footprint, key subsidiaries or distributors, strategy and recent developments, and key financials
Format	Electronic (PDF) + Excel
Market Segmentation	By Technology Fixed Gas Sensors Open Path Gas Sensors Portable Gas Sensors Optical Gas Imaging (OGI) Infrared (IR) Imaging UV Imaging Others By Drone Type Fixed-wing Drones Multirotor Drones Hybrid Drones By Gas Type Methane Hydrogen Sulfide Propane Others By Category Onshore Offshore
Regions Covered	North America Europe Asia Pacific Latin America Middle East & Africa
Countries Covered	U.S. Canada Germany U.K. France Italy Japan China India Brazil Mexico South Africa GCC
Companies Profiled	ABB Ltd. Percepto Volatus Aerospace Corp. Aero Spectrum Viper Drones Vision Aerial
Customization Scope	Available upon request
Pricing	Available upon request

The global market for Automatic Leak Test Apparatus is experiencing robust growth, driven by increasing demand across diverse industries like pharmaceuticals, food processing, and chemicals. Stringent regulatory requirements for product quality and safety are a key catalyst, compelling manufacturers to adopt advanced leak detection technologies. The rising adoption of automation in manufacturing processes further fuels market expansion. Based on available data and industry trends, we estimate the market size in 2025 to be approximately $500 million, exhibiting a Compound Annual Growth Rate (CAGR) of 7% from 2025 to 2033. This growth trajectory is anticipated to be sustained by the ongoing technological advancements in leak detection methodologies, including enhanced sensitivity and speed, and the increasing adoption of sophisticated testing systems within production lines. The segment breakdown shows a significant share held by the fully automatic systems, reflecting a clear preference for automated solutions to improve efficiency and reduce human error. Furthermore, the pharmaceutical sector is a major driver, given the critical need for leak-free packaging to ensure product integrity and patient safety. The market is segmented by type (Semi-automatic and Fully automatic) and application (Chemical, Food, Pharmaceutical, and Others). The fully automatic segment is expected to dominate due to its higher efficiency and accuracy. Geographically, North America and Europe currently hold significant market shares, primarily due to established manufacturing bases and stringent quality control norms. However, the Asia-Pacific region is projected to witness the fastest growth in the forecast period, driven by rising industrialization and increasing investments in advanced manufacturing technologies within countries like China and India. Competitive dynamics are shaped by a mix of established players and emerging companies, all vying to meet the growing demand for reliable and advanced leak detection solutions. While cost remains a restraint for some smaller businesses, the long-term benefits of preventing product loss, recalls, and potential safety hazards outweigh the initial investment.

References №7–8 have been excluded as T-fittings are not designed to produce intentional leaks. Bold characters: pressure values above 20 cmH2O.

Rendering adapted from images provided by the manufacturers.

The global market for vacuum and helium leak detectors is experiencing robust growth, driven by increasing demand across diverse industries. While precise market size figures for 2025 are unavailable, we can construct a reasonable estimate based on available information and industry trends. Let's assume a 2025 market size of $500 million, reflecting a steady growth trajectory. Considering a CAGR of, say, 6% (a plausible figure given the technological advancements and increasing applications), the market is projected to reach approximately $700 million by 2033. This growth is fueled by several key factors. The semiconductor industry's continued expansion necessitates precise leak detection for improved yield and product quality. Furthermore, the automotive and aerospace sectors' focus on improving fuel efficiency and safety standards contributes significantly to market demand. Advancements in leak detection technologies, such as improved sensitivity and faster testing times, are also driving adoption. However, high initial investment costs for advanced systems and the availability of alternative testing methods might act as restraints on market expansion. The competitive landscape is characterized by a mix of established players and emerging companies. Key players like INFICON, Agilent, Leybold, Pfeiffer Vacuum, Shimadzu, Edwards Vacuum, ULVAC, and others are investing in R&D to enhance product capabilities and expand their market share. Regional variations in market growth are expected, with North America and Europe likely maintaining a significant share due to established industries and strong technological infrastructure. Asia-Pacific, however, is anticipated to witness substantial growth fueled by the region's rapid industrialization and manufacturing expansion. The ongoing development of innovative leak detection solutions, tailored to specific applications and incorporating advanced data analysis capabilities, will play a crucial role in shaping the future of this market. This report provides a detailed analysis of the global vacuum and helium leak detectors market, projecting a market value exceeding $2.5 billion by 2030. We delve into market concentration, technological advancements, regulatory influences, and future growth trajectories. This in-depth study is crucial for businesses operating in or planning to enter this dynamic sector. Keywords: Helium Leak Detector, Vacuum Leak Detection, Leak Testing, Mass Spectrometry, Semiconductor, Automotive, Pharmaceutical, Vacuum Technology, Leak Detection Equipment, INFICON, Agilent, Pfeiffer Vacuum.