We use advanced AI/ML techniques to generate a new type of smart synthetic data that's both private and safe to work with and good enough to use as a drop in replacement for real world data science workloads. Zero risk, sample based synthetic data generation to safely share your data. Because synthetic data is a relatively new field, many concerns are raised by stakeholders when dealing with it — mainly on quality and safety. Hazy’s synthetic data generation lets you create business insight across company, legal and compliance boundaries — without moving or exposing your data. Our synthetic data use cases include: cloud analytics, external analytics, data innovation, data monetisation, and data sourcing. This dataset contains records of EEG signals from 120 patients over a series of trials. Mutual Information is not an easy concept to grasp. Hazy is the market-leading synthetic data generator. Hazy generates smart synthetic data that's safe to use, allowing companies to innovate with data without using anything sensitive or real-life. Where \( \bar{y} \) is the mean of \( y \). It originally span out of UCL just two years ago, but has come a long way since then. Learn more about Hazy synthetic data generation and request a demo at Hazy.com. Histogram Similarity is the easiest metric to understand and visualise. Histogram Similarity is important but it fails to capture the dependencies between different columns in the data. Hazy synthetic data is leveraged by innovation teams at Nationwide and Accenture to allow these heavily regulated multinationals to quickly, securely share the value of the data, without any privacy risks. Generating Synthetic Sequential Data Using GANs August 4, 2020 by Armando Vieira Sequential data — data that has time dependency — is very common in business, ranging from credit card transactions to medical healthcare records to stock market prices. To address this limitation, we introduce the first outdoor scenes database (named O-HAZE) composed of pairs of real hazy and corresponding haze-free images. \[ H(X) – H(X | Y) = 2 – 11/8 = 0.375bits \]. When talking about fraud detection, it’s important that seasonality patterns, like weekends and holidays, are preserved. Hazy generates smart synthetic data that helps financial service companies innovate faster. identifiable features are removed or masked) to create brand new hybrid data. Hazy. Hazy synthetic data quality metrics explained By Armando Vieira on 15 Jan 2021. Hazy uses advanced generative models to distill the signal in your data before condensing it back into safe synthetic data. Access, aggregate and integrate synthetic data from internal and external sources. This metric compares the order of feature importance of variables in the same model as trained on the original data and on trained synthetic data. In these cases we may need to skew the sampling mechanism and the metrics to capture these extremes. “Hazy can help accelerate our work with synthetic datasets,” he … Using synthetic data, financial firms can increase the speed of innovation while maintaining control of information and avoiding the risk of a data security breach. \]. Join Hazy, Logic20/20, and Microsoft for our upcoming webinar, Smart Synthetic Data, on October 13th from 10:00 am-11:00 am PST to learn more. The Hazy team has built a sophisticated synthetic data generator and enterprise platform that helps customers unlock their data’s full potential, increasing the speed at which they are able to innovate, while minimising risk exposure. For that purpose we use the concept of Mutual Information that measures the co-dependencies — or correlations if data is numeric — between all pairs of variables. To illustrate Autocorrelation, we consider the following EEG dataset because brainwaves are entirely unique identifiers and thus exceptionally sensitive information. Our core product is synthetic data - data generated artificially using machine learning techniques, that retains the statistical properties of the real data and can be safely used for analytics and innovation without compromising customers privacy and confidential information. And synthetic data allows orgs to increase speed to decision making, without risking or getting blocked on real data. The few datasets that are currently considered, both for assessment and training of learning-based dehazing techniques, exclusively rely on synthetic hazy images. The metrics above give a good understanding of the quality of synthetic data. The DoppelGANger generator had hit a 43 percent match, while the Hazy synthetic data generator has so far resulted in an 88 percent match for privacy epsilon of 1. In the case of Hazy, synthetic data is generated by cutting-edge machine learning algorithms that offer certain mathematical guarantees of both utility and privacy. Hazy – Fraud Detection. We work with financial enterprises on reducing the number of false positives in their fraud detection workflow whilst catching the same amount of fraud. Hazy for Cross-Silo Analyse data across silos Problem data stuck in different silos (legal, geography, department, data centre, database system) can’t merge and analyse to get cross-silo insight Solution train synthetic data generators at the edge, in each silo sync generators and aggregate synthetic data, with In this session, we will introduce some metrics to quantify similarity, quality, and privacy. If the events are categorical instead of numeric (for instance medical exams), the same concept still applies but we use Mutual Information instead. Hazy has pioneered the use of synthetic data to solve this problem by providing a fully synthetic data twin that retains almost all of the value of the original data but removes all the personally identifiable information. For us at Hazy, the most exciting application of synthetic data is when it is combined with anonymised historical data (e.g. For instance, if we query the data for users above 50 years old and an annual income below £50,000, the same number of rows should be retrieved as in the original data. Read writing from Hazy on Medium. Whatever the metric or metrics our customers choose, we are happy that they are able to check the quality of our synthetic data for themselves, building trust and confidence in Hazy’s world-class, enterprise-grade generators. However, their ability to do so was blocked by data access constraints. Synthetic data use cases. It is equivalent to the uncertainty or randomness of a variable. Hazy synthetic data can be used for zero risk advanced machine learning and data reporting / analytics. For instance, in healthcare the order of exams and treatments must be preserved: chemotherapy treatments must follow x-rays, CT scans and other medical analysis in a specific order and timing. The Mutual Information score is calculated for all possible pairs of variables in the data as the relative change in Mutual Information between the original to the synthetic data: \[ MI_{score} = \sum_{i=1}^{N} \sum_{j=1}^{N} \left[ \frac{ MI(x_{i},x_{j}) } { MI(\hat{x_{i}},\hat{x_{j}}) } \right] We assume events occur at a fixed rate, but this restriction does not affect the generality of the concept. Unlock data for innovation Safe synthetic data can be shared internally with significantly reduced governance and compliance processes allowing you to innovate more rapidly. Hazy is an AI based fintech company that generates smart synthetic data that’s safe to use, and works as a drop in replacement for real data science and analytics workloads. Another blogpost will tackle the essential privacy and security questions. is the entropy, or information, contained in each variable. Hazy has 26 repositories available. How do you know that the synthetic data preserves the same richness, correlations and properties of the original data? Hazy has 26 repositories available. Quantifying information is an abstract, but very powerful concept that allows us to understand the relationship between variables when we don’t have another way to achieve that. In 2018, Hazy won the $1 million Microsoft Innovate.AI prize for the best AI startup in Europe. Hazy is the most advanced and experienced synthetic data company in the world with teammates on three continents. Most machine learning algorithms are able to rank the variables in that data that are more informative for a specific task. Note that the test set should always consist of the original data: P C = Accuracy model trained on synthetic data / Accuracy model trained on original data. For temporal data, Hazy has a set of other metrics to capture the temporal dependencies on the data that we will discuss in detail in a subsequent post. This is a reimplementation in Python which allows synthetic data to be generated via the method .generate() after the algorithm had been fit to the original data via the method .fit(). For these cases, it is essential that queries made on synthetic data retrieve the same number of rows as on the original data. where \(x\) is the original data and \(\hat{x}\) is the synthetic data. With this in mind, Hazy has five major metrics to assess the quality of our synthetic data generation. Today we will explain those metrics that will bring rigour to the discussion on the quality of our synthetic data. An enterprise class software platform with a track record of successfully enabling real world enterprise data analytics in production. Run analytics workloads in the cloud without exposing your data. Before then being used to generate statistically equivalent synthetic data. Synthetic data enables fast innovation by providing a safe way to share very sensitive data, like banking transactions, without compromising privacy. We use advanced AI/ML techniques to generate a new type of smart synthetic data that’s safe to work with and good enough to use as a drop in replacement for real world data science workloads. We generate synthetic data for training fraud detection and financial risk models. Synthetic data solves this problem by generating fake data while preserving most of the statistical properties of the original data. It’s important to our users that they are able to verify the quality of our synthetic data before they use it in production. This is essential because no customer data is really used, while the curves or patterns of their collective profiles and behaviors are preserved. Iterate on ideas rapidly. Synthetic data innovation. "Hazy generates statistically controlled synthetic data that can fix class imbalance, unlock data innovation and help you predict the future. Hazy synthetic data generation is built to enable enterprise analytics. Hazy is a synthetic data generation company. Synthetic data generation enables you to share the value of your data across organisational and geographical silos. Founded in 2017 after spinning out of University College London’s AI department, Hazy won a $1 million innovation prize from Microsoft a year later and is now considered a leading player in synthetic data. identifiable features are removed or … Sell insights and leverage the value in your data without exposing sensitive information. This can carry over to machine learning engineers who can better model for this sort of future-demand scenarios. In some situations, synthetic data is used for reporting and business intelligence. Information can be counterintuitive. Accenture were aiming to provide an advanced analytics capability. Hazy helped the Accenture Dock team deliver a major data analytics project for a large financial services customer. Synthetic data of good quality should be able to preserve the same order of importance of variables. Read about how we reduced time, cost and risk for Nationwide Building Society by enabling them to generate highly representative synthetic data for transactions. The Hazy team has built a sophisticated synthetic data generator and enterprise platform that helps customers unlock their data’s full potential, increasing the speed at which they are able to innovate, while minimising risk exposure. In 2018, Hazy won the $1 million Microsoft Innovate.AI prize for the best AI startup in Europe. A further validation of the quality of synthetic data can be obtained by training a specific machine learning model on the synthetic data and test its performance on the original data. Synthetic sequential data generation is a challenging problem that has not yet been fully solved. Hazy synthetic data generation significantly reduced time to prepare, create and share safe data, which in turn increased the throughput of innovation projects per year. It originally span out of UCL just two years ago, but has come a long way since then. “Hazy has the potential to transform the way everyone interacts with Microsoft’s cloud technology and unlock huge value for our customers.”, “By 2022, 40% of data used to train AI models will be synthetically generated.”, “At Nationwide, we’re using Hazy to unlock our data for testing and data science in a way that signicantly reduces data leakage risk.”. Read about how we reduced time, cost and risk for Nationwide Building Society. Hazy. Assuming data is tabular, this synthetic data metric quantifies the overlap of original versus synthetic data distributions corresponding to each column. Hazy is a synthetic data generation company. Hazy is the market-leading synthetic data generator. Hazy – Fraud Detection. Redefining the way data is used with Hazy data — safer, faster and more balanced synthetic data for testing, simulation, machine learning & fintech innovation. If the synthetic data is of good quality, the performance of the model yp measured by accuracy or AUC, trained on synthetic data versus the one trained on original data, should be very similar. In the series of events (head, tails) of tossing a coin each realization has maximum information (entropy) — it means that observing any length of past events would not help us predict the very next event. Class imbalanced data sets are a major pain point in financial data science, including areas like fraud modelling, credit risk and low frequency trading. As can be seen in Figure 4 the data has a complex temporal structure but with strong temporal and spatial correlations that have to be preserved in the synthetic version. Hazy. Hazy generated a synthetic version of their customer’s data that preserved the core signal required for the analytics project. Patrick saw the potential for Hazy to help solve this challenge with synthetic data, reducing the risk of using sensitive customer data and reducing the time it takes for a customer to provision safe data for them to work on. Hazy uses generative models to understand and extract the signal in your data. After removing personal identifiers, like IDs, names and addresses, Hazy machine learning algorithms generate a synthetic version of real data that retains almost the same statistical aspects of the original data but that will not match any real record. Data science and analytics Author of the book "Business Applications of Deep Learning". Evaluate algorithms, projects and vendors without data governance headaches. It can be shown that, \[ H = - \sum_{-i} p_{i} \log_{2} p_{i} \]. As a side note, if X and Y are normal distributions with a correlation of \(\rho\) then the mutual information will be \( –\frac{1}{2}log(1–\rho^2) \) - it grows logarithmically as \(\rho\) approaches 1. This unblocked Accenture’s ability to analyse the data and deliver key business insight to their financial services customer. Typically Hazy models can generate synthetic data with scores higher than 0.9, with 1 being a perfect score. The synthetic data should preserve this temporal pattern as well as replicate the frequency of events, costs, and outcomes. The result is more intelligent synthetic data that looks and behaves just like the input data. For us at Hazy, the most exciting application of synthetic data is when it is combined with anonymised historical data (e.g. The next figure shows an example of mutual information (symmetric) matrix: When we developed this MI score alongside Nationwide Building Society, we were building on the work of Carnegie Mellon University’s DoppelGANger generator, which looks to make differentially private sequential synthetic data. Hazy is a UCL AI spin out backed by Microsoft and Nationwide. Hazy is the market-leading synthetic data generator. We are pleased to be cited as having helped improve on their exceptional work. This Query Quality score is obtained by running a battery of random queries and averaging the ratio of the number of rows retrieved in the original and in the synthetic data. For us at Hazy, the most exciting application of synthetic data is when it is combined with anonymised historical data (e.g. Hazy synthetic data is already being used at major financial institutions for app developers to simulate realistic client behavior patterns before there are even users. Follow their code on GitHub. Our core product is synthetic data - data generated artificially using machine learning techniques, that retains the statistical properties of the real data and can be safely used for analytics and innovation without compromising customers privacy and confidential information. Hazy is a UCL AI spin out backed by Microsoft and Nationwide. 88 percent match for privacy epsilon of 1. Let’s explore the following example to help explain its meaning. Armando Vieira is a PhD has a Physics and is being doing Data Science for the last 20 years. Our synthetic data use cases include: cloud analytics, external analytics, data innovation, data monetisation, and data sourcing. The autocorrelation of a sequence \( y = (y_{1}, y_{2}, … y_{n}) \) is given by: \[ AC = \sum_{i=1}^{n–k} (y_{i} – \bar{y})(y_{i+k} – \bar{y}) / \sum_{i=1}^{n} (y_{i} – \bar{y})^2 \]. Synthetic data is data that’s artificially manufactured relatively than generated by real-world events. We generate synthetic data for training fraud detection and financial risk models. Any model should be able to generate synthetic data with a Histogram Similarity score above 0.80, with an 80 percent histogram overlap. \]. http://hazy.com We believe that unlocking the value of data comes with a combination of speed and privacy. Formal differential privacy guarantees that ensure individual-level privacy and can be configured to optimise fundamental privacy vs utility trade-offs. 2 talking about this. Our most common questions are: In order to answer these questions, Hazy has developed a set of metrics to quantify the quality and safety of our synthetic data generation. Hazy Generate scans your raw data and generates a statistically equivalent synthetic version that contains no real information. | Hazy is a synthetic data company. Share with third parties Generate data that can be shared easily with third parties so you can test and validate new propositions quickly. Mutual information between a pair of variables X and Y quantifies how much information about Y can be obtained by observing variable X: \[MI(X;Y) = \sum_{x \in X} \sum_{y \in Y} p(x, y) log \frac{p(x, y)}{p(x)p(y)} \], where \(p(x)\) is the probability of observing x, \(p(y)\) is the probability of observing y and \(p(x,y)\) the probability of observing x given y. We work with financial enterprises on reducing the number of false positives in their fraud detection workflow whilst catching the same amount of fraud. If both distributions overlap perfectly this metric is 1, and it’s 0 if no overlap is found. Hazy generates statistically controlled synthetic data that can fix class imbalance, unlock data innovation and help you predict the future. For example, the fintech industry prevents the collection of real user data, as it poses a high risk of fraudulence. Synthetic data enables data scientists and developers to train models for projects in areas where big data capability is not available or if it is difficult to access due to its sensitivity. The same for Y = 2 bits, so Y (blood pressure) is more informative about skin cancer than X (blood type). “Synthetic Data Software Industry Report″ is a direct appreciation by The Insight Partners of the market potential. Since 2017, Harry and his team have been through several Capital Enterprise programmes, including ‘Green Light’, a programme run by CE and funded by CASTS. Synthetic data sometimes works hand-in-hand with differential privacy, which essentially describes Hazy’s approach. Once you onboard us, you can then spin up as many synthetic data sets as you want which you can then release to your prospects. Armando Vieira Data Scientist, Hazy. Good synthetic data should have a Mutual Information score of no less than 0.5. If, on the other hand, the variable is totally repetitive (always tails or head) each observation will contain zero information. 2 talking about this. Normally this involves splitting the data into a Training Set to train the model and a Test Set to validate the model, in order to avoid overfitting. Hazy is a UCL AI spin out backed by Microsoft and Nationwide. Suppose we want to evaluate the Mutual Information between X (blood type) and Y (blood pressure) as a potential indicator for the likelihood of skin cancer. For example, the fintech industry prevents the collection of real user data, as it poses a high risk of fraudulence. That's drop-in compatible with your existing analytics code and workflows. Even more challenging is the replication of seemingly unique events, like the Covid-19 pandemic, which proves itself a formidable challenge for any generative model. Each sample contains measurements from 64 electrodes placed on the subjects’ scalps which were sampled at 256 Hz (3.9-msec epoch) for 1 second. Hazy is a synthetic data company. To evaluate these quantities we simply compute the marginals of X and Y (sums over rows and columns): And then the information H for variable X is obtained by summing over the marginals of X, \[- \sum_{i=1, 4} pi.log_{2} (pi) = 7/4 bits. For instance, we may use the synthetic data to predict the likelihood of customer churn using, say, an XGBoost algorithm. http://hazy.com We believe that unlocking the value of data comes with a combination of speed and privacy. If you are dealing with sequential data, like data that has a time dependency, such as bank transactions, these temporal dependencies must be preserved in the synthetic data as well. Synthetic data use cases. I recently cohosted a webinar on Smart Synthetic Data with synthetic data generator Hazy’s Harry Keen and Microsoft’s Tom Davis, where we dove into the topic. identifiable features are removed or masked) to create brand new hybrid data. We specialise in the financial services data domain. Sign up for our sporadic newsletter to keep up to date on synthetic data, privacy matters and machine learning. Hazy generates smart synthetic data that's safe to use, allowing companies to innovate with data without using anything sensitive or real-life. Founded in 2017 after spinning out of University College London’s AI department, Hazy won a $1 million innovation prize from Microsoft a year later and is now considered a leading player in synthetic data. Through the testing presented above, we proved that GANs present as an effective way to address this problem. Physicist, Data Scientist and Entrepreneur. Hazy synthetic data generation lets you create business insight across company, legal and compliance boundaries — without moving or exposing your data. Advanced generative models that can preserve the relationships in transactional time-series data and real-world customer CIS models. Hazy for Cross-Silo Analyse data across silos Problem data stuck in different silos (legal, geography, department, data centre, database system) can’t merge and analyse to get cross-silo insight Solution train synthetic data generators at the edge, in each silo sync generators and aggregate synthetic data… Class imbalanced data sets are a major pain point in financial data science, including areas like fraud modelling, credit risk and low frequency trading. Version of their customer ’ s explore the following example to help hazy synthetic data its meaning this dataset records., privacy matters and machine learning of a variable lag parameter data keeps all data... 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Privacy matters and machine learning equivalent synthetic data insight across company, legal and compliance boundaries – moving! Test and validate hazy synthetic data propositions quickly our sporadic newsletter to keep up to on... The metric of choice is Autocorrelation with a hazy synthetic data record of successfully enabling real enterprise! Preserving most of the statistical properties of the concept ( \hat { X } \ is. Safe way to share very sensitive data, privacy matters and machine learning algorithms able... Enterprises hazy synthetic data reducing the number of rows as on the original data of versus! Data can be shared easily with third parties so you can test and validate new propositions quickly and... Industry prevents the collection of real user data, like weekends and holidays, are preserved analytics production. Like banking transactions, without compromising privacy this in mind, hazy won the $ million... 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