Author of the book "Business Applications of Deep Learning". It originally span out of UCL just two years ago, but has come a long way since then. In some situations, synthetic data is used for reporting and business intelligence. Histogram Similarity is important but it fails to capture the dependencies between different columns in the data. Access specialist external data analysts and externally hosted tools and services. The report intends to provide accurate and meaningful insights, both quantitative as well as qualitative of Synthetic Data Software Market. 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. Hazy is a synthetic data company. Zero risk, sample based synthetic data generation to safely share your data. Hazy is the market-leading synthetic data generator. 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 | 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. We work with financial enterprises on reducing the number of false positives in their fraud detection workflow whilst catching the same amount of fraud. In this session, we will introduce some metrics to quantify similarity, quality, and privacy. Hazy is the most advanced and experienced synthetic data company in the world with teammates on three continents. 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. Our synthetic data use cases include: cloud analytics, external analytics, data innovation, data monetisation, and data sourcing. Our synthetic data use cases include: cloud analytics, external analytics, data innovation, data monetisation, and data sourcing. Typically Hazy models can generate synthetic data with scores higher than 0.9, with 1 being a perfect score. 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. For these cases, it is essential that queries made on synthetic data retrieve the same number of rows as on the original data. Hazy helped the Accenture Dock team deliver a major data analytics project for a large financial services customer. Information can be counterintuitive. For example, the fintech industry prevents the collection of real user data, as it poses a high risk of fraudulence. Because synthetic data is a relatively new field, many concerns are raised by stakeholders when dealing with it — mainly on quality and safety. For us at Hazy, the most exciting application of synthetic data is when it is combined with anonymised historical data (e.g. 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 2018, Hazy won the $1 million Microsoft Innovate.AI prize for the best AI startup in Europe. Advanced GAN technology Hazy Generate incorporates advanced deep learning technology to generate highly accurate safe data. It originally span out of UCL just two years ago, but has come a long way since then. Hazy’s synthetic data generation lets you create business insight across company, legal and compliance boundaries — without moving or exposing your data. We work with financial enterprises on reducing the number of false positives in their fraud detection workflow whilst catching the same amount of fraud. 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. 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. Hazy has 26 repositories available. Hazy is a synthetic data generation company. Armando Vieira is a PhD has a Physics and is being doing Data Science for the last 20 years. For example, the fintech industry prevents the collection of real user data, as it poses a high risk of fraudulence. Armando Vieira Data Scientist, Hazy. Synthetic data sometimes works hand-in-hand with differential privacy, which essentially describes Hazy’s approach. 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. Synthetic data generation enables you to share the value of your data across organisational and geographical silos. “Hazy can help accelerate our work with synthetic datasets,” he … 2 talking about this. Read writing from Hazy on Medium. 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. Synthetic data sometimes works hand-in-hand with differential privacy, which essentially describes Hazy’s approach. Read about how we reduced time, cost and risk for Nationwide Building Society. The result is more intelligent synthetic data that looks and behaves just like the input data. The autocorrelation of a sequence \( y = (y_{1}, y_{2}, … y_{n}) \) is given by: \[ AC = \sum_{i=1}^{n–k} (y_{i} – \bar{y})(y_{i+k} – \bar{y}) / \sum_{i=1}^{n} (y_{i} – \bar{y})^2 \]. Hazy synthetic data can be used for zero risk advanced machine learning and data reporting / analytics. 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. 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. identifiable features are removed or masked) to create brand new hybrid data. Follow their code on GitHub. 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. Class imbalanced data sets are a major pain point in financial data science, including areas like fraud modelling, credit risk and low frequency trading. Today we will explain those metrics that will bring rigour to the discussion on the quality of our synthetic data. 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. Mutual Information is not an easy concept to grasp. Hazy synthetic data generation lets you create business insights across company, legal and compliance boundaries – without moving or exposing your data. Before then being used to generate statistically equivalent synthetic data. 2 talking about this. Hazy synthetic data generation lets you create business insights across company, legal and compliance boundaries – without moving or exposing your data. That's drop-in compatible with your existing analytics code and workflows. Hazy uses generative models to understand and extract the signal in your data. 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] 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. 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. Hazy | 1 429 abonnés sur LinkedIn. Redefining the way data is used with Hazy data — safer, faster and more balanced synthetic data for testing, simulation, machine learning & fintech innovation. Where \( \bar{y} \) is the mean of \( y \). Another blogpost will tackle the essential privacy and security questions. Advanced generative models that can preserve the relationships in transactional time-series data and real-world customer CIS models. Synthetic data innovation. We assume events occur at a fixed rate, but this restriction does not affect the generality of the concept. identifiable features are removed or … Evaluate algorithms, projects and vendors without data governance headaches. Hazy synthetic data generation lets you create business insight across company, legal and compliance boundaries — without moving or exposing your data. 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. 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. Physicist, Data Scientist and Entrepreneur. 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. If both distributions overlap perfectly this metric is 1, and it’s 0 if no overlap is found. Formal differential privacy guarantees that ensure individual-level privacy and can be configured to optimise fundamental privacy vs utility trade-offs. “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.”. To illustrate Autocorrelation, we consider the following EEG dataset because brainwaves are entirely unique identifiers and thus exceptionally sensitive information. 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. Hazy. If the events are categorical instead of numeric (for instance medical exams), the same concept still applies but we use Mutual Information instead. 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 generates statistically controlled synthetic data that can fix class imbalance, unlock data innovation and help you predict the future. Hazy uses advanced generative models to distill the signal in your data before condensing it back into safe synthetic data. It is equivalent to the uncertainty or randomness of a variable. Synthetic sequential data generation is a challenging problem that has not yet been fully solved. 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. 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. 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. Synthetic data enables fast innovation by providing a safe way to share very sensitive data, like banking transactions, without compromising privacy. Hazy is a synthetic data generation company. Autocorrelation basically measures how events at time \( X(t) \) are related to events at time \( X(t - \delta) \) where \( \delta \) is a lag parameter. The metrics above give a good understanding of the quality of synthetic data. Synthetic data innovation. \]. In other words, the synthetic data keeps all the data value while not compromising any of the privacy. We specialise in the financial services data domain. Synthetic data solves this problem by generating fake data while preserving most of the statistical properties of the original data. We generate synthetic data for training fraud detection and financial risk models. Synthetic data use cases. 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. Sell insights and leverage the value in your data without exposing sensitive information. We generate synthetic data for training fraud detection and financial risk models. 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. 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. Access, aggregate and integrate synthetic data from internal and external sources. We are pleased to be cited as having helped improve on their exceptional work. And synthetic data allows orgs to increase speed to decision making, without risking or getting blocked on real data. Learn more about Hazy synthetic data generation and request a demo at Hazy.com. 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. This is essential because no customer data is really used, while the curves or patterns of their collective profiles and behaviors are preserved. Read about how we reduced time, cost and risk for Nationwide Building Society by enabling them to generate highly representative synthetic data for transactions. These models can then be moved safely across company, legal and compliance boundaries. 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. 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. Hazy Generate scans your raw data and generates a statistically equivalent synthetic version that contains no real information. Contribute to hazy/synthpop development by creating an account on GitHub. 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. 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. In the example below, we see that within Hazy you are able to see the level of importance set by the algorithm and how accurately Hazy retains that level. Hazy generates smart synthetic data that helps financial service companies innovate faster. Assuming data is tabular, this synthetic data metric quantifies the overlap of original versus synthetic data distributions corresponding to each column. 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. Hazy is a UCL AI spin out backed by Microsoft and Nationwide. For us at Hazy, the most exciting application of synthetic data is when it is combined with anonymised historical data (e.g. This can carry over to machine learning engineers who can better model for this sort of future-demand scenarios. Class imbalanced data sets are a major pain point in financial data science, including areas like fraud modelling, credit risk and low frequency trading. 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 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. Most machine learning algorithms are able to rank the variables in that data that are more informative for a specific task. However, their ability to do so was blocked by data access constraints. 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