Synthetic data use cases. 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. Most machine learning algorithms are able to rank the variables in that data that are more informative for a specific task. Formal differential privacy guarantees that ensure individual-level privacy and can be configured to optimise fundamental privacy vs utility trade-offs. “Synthetic Data Software Industry Report″ is a direct appreciation by The Insight Partners of the market potential. "Hazy generates statistically controlled synthetic data that can fix class imbalance, unlock data innovation and help you predict the future. Learn more about Hazy synthetic data generation and request a demo at Hazy.com. How can we be sure the synthetic data is really safe and can’t be reverse engineered to disclose private information. Hazy is a UCL AI spin out backed by Microsoft and Nationwide. 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(). This unblocked Accenture’s ability to analyse the data and deliver key business insight to their financial services customer. identifiable features are removed or masked) to create brand new hybrid 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. Hazy synthetic data generation lets you create business insights across company, legal and compliance boundaries – without moving or exposing your data. Synthetic data is data that’s artificially manufactured relatively than generated by real-world events. Contribute to hazy/synthpop development by creating an account on GitHub. However, their ability to do so was blocked by data access constraints. 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. Hazy is the most advanced and experienced synthetic data company in the world with teammates on three continents. For us at Hazy, the most exciting application of synthetic data is when it is combined with anonymised historical data (e.g. \[ H(X) – H(X | Y) = 2 – 11/8 = 0.375bits \]. 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. For these cases, it is essential that queries made on synthetic data retrieve the same number of rows as on the original data. 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. 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 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. 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. Hazy is the market-leading synthetic data generator. 2 talking about this. Hazy has 26 repositories available. Synthetic data innovation. Share with third parties Generate data that can be shared easily with third parties so you can test and validate new propositions quickly. “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.”. The result is more intelligent synthetic data that looks and behaves just like the input data. Hazy generates smart synthetic data that helps financial service companies innovate faster. 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. 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. It can be shown that, \[ H = - \sum_{-i} p_{i} \log_{2} p_{i} \]. 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. Access, aggregate and integrate synthetic data from internal and external sources. 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. 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. 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. Hazy helped the Accenture Dock team deliver a major data analytics project for a large financial services customer. Read about how we reduced time, cost and risk for Nationwide Building Society. When talking about fraud detection, it’s important that seasonality patterns, like weekends and holidays, are preserved. Synthetic data sometimes works hand-in-hand with differential privacy, which essentially describes Hazy’s approach. Synthetic data comes with proven data compliance and risk mitigation. For example, the fintech industry prevents the collection of real user data, as it poses a high risk of fraudulence. For instance, we may use the synthetic data to predict the likelihood of customer churn using, say, an XGBoost algorithm. Synthetic data sometimes works hand-in-hand with differential privacy, which essentially describes Hazy’s approach. Physicist, Data Scientist and Entrepreneur. Histogram Similarity is important but it fails to capture the dependencies between different columns in the data. Hazy is a synthetic data generation company. This is essential because no customer data is really used, while the curves or patterns of their collective profiles and behaviors are preserved. If both distributions overlap perfectly this metric is 1, and it’s 0 if no overlap is found. 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. 88 percent match for privacy epsilon of 1. Where \( \bar{y} \) is the mean of \( y \). Hazy is the market-leading synthetic data generator. Sign up for our sporadic newsletter to keep up to date on synthetic data, privacy matters and machine learning. Another blogpost will tackle the essential privacy and security questions. Synthetic data innovation. Sign up for our sporadic newsletter to keep up to date on synthetic data, privacy matters and machine learning. 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. 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. 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. Run analytics workloads in the cloud without exposing your data. 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. Mutual Information is not an easy concept to grasp. For example, the fintech industry prevents the collection of real user data, as it poses a high risk of fraudulence. 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. 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. Our synthetic data use cases include: cloud analytics, external analytics, data innovation, data monetisation, and data sourcing. Armando Vieira Data Scientist, Hazy. This dataset contains records of EEG signals from 120 patients over a series of trials. However, some caution is necessary as, in some cases, a few extreme cases may be overwhelmingly important and, if not captured by the generator, could render the synthetic data useless — like rare events for fraud detection or money laundering. Hazy is the market-leading synthetic data generator. 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. Typically Hazy models can generate synthetic data with scores higher than 0.9, with 1 being a perfect score. With this in mind, Hazy has five major metrics to assess the quality of our synthetic data generation. Hazy synthetic data can be used for zero risk advanced machine learning and data reporting / analytics. 2 talking about this. Hazy. Hazy. We assume events occur at a fixed rate, but this restriction does not affect the generality of the concept. 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. Because synthetic data is a relatively new field, many concerns are raised by stakeholders when dealing with it — mainly on quality and safety. Author of the book "Business Applications of Deep Learning". Zero risk, sample based synthetic data generation to safely share your 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. “Hazy can help accelerate our work with synthetic datasets,” he … 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. Hazy uses advanced generative models to distill the signal in your data before condensing it back into safe synthetic data. 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. Class imbalanced data sets are a major pain point in financial data science, including areas like fraud modelling, credit risk and low frequency trading. In this session, we will introduce some metrics to quantify similarity, quality, and privacy. | Hazy is a synthetic data company. Synthetic data use cases. Hazy has 26 repositories available. An enterprise class software platform with a track record of successfully enabling real world enterprise data analytics in production. Class imbalanced data sets are a major pain point in financial data science, including areas like fraud modelling, credit risk and low frequency trading. Hazy’s synthetic data generation lets you create business insight across company, legal and compliance boundaries — without moving or exposing your data. It originally span out of UCL just two years ago, but has come a long way since then. Hazy synthetic data generation lets you create business insights across company, legal and compliance boundaries – without moving or exposing your data. Hazy synthetic data generation lets you create business insight across company, legal and compliance boundaries — without moving or exposing your data. where \(x\) is the original data and \(\hat{x}\) is the synthetic data. 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. We work with financial enterprises on reducing the number of false positives in their fraud detection workflow whilst catching the same amount of fraud. Iterate on ideas rapidly. In some situations, synthetic data is used for reporting and business intelligence. And synthetic data allows orgs to increase speed to decision making, without risking or getting blocked on real data. We generate synthetic data for training fraud detection and financial risk models. Zero risk, sample based synthetic data generation to safely share your data. Synthetic sequential data generation is a challenging problem that has not yet been fully solved. Hazy generated a synthetic version of their customer’s data that preserved the core signal required for the analytics project. In these cases we may need to skew the sampling mechanism and the metrics to capture these extremes. 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. 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. Any model should be able to generate synthetic data with a Histogram Similarity score above 0.80, with an 80 percent histogram overlap. Accenture were aiming to provide an advanced analytics capability. \]. is the entropy, or information, contained in each variable. Let’s explore the following example to help explain its meaning. 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 2018, Hazy won the $1 million Microsoft Innovate.AI prize for the best AI startup in Europe. Hazy is a synthetic data generation company. Advanced generative models that can preserve the relationships in transactional time-series data and real-world customer CIS models. Read writing from Hazy on Medium. How do you know that the synthetic data preserves the same richness, correlations and properties of the original data? We are pleased to be cited as having helped improve on their exceptional work. Hazy generates statistically controlled synthetic data that can fix class imbalance, unlock data innovation and help you predict the future. Advanced GAN technology Hazy Generate incorporates advanced deep learning technology to generate highly accurate safe data. For us at Hazy, the most exciting application of synthetic data is when it is combined with anonymised historical data (e.g. These models can then be moved safely across company, legal and compliance boundaries. 2 talking about this. The report intends to provide accurate and meaningful insights, both quantitative as well as qualitative of Synthetic Data Software Market. 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. To illustrate Autocorrelation, we consider the following EEG dataset because brainwaves are entirely unique identifiers and thus exceptionally sensitive information. Hazy | 1 429 abonnés sur LinkedIn. Information can be counterintuitive. 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] Armando Vieira is a PhD has a Physics and is being doing Data Science for the last 20 years. Hazy uses generative models to understand and extract the signal in your data. Evaluate algorithms, projects and vendors without data governance headaches. Histogram Similarity is the easiest metric to understand and visualise. 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 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. Redefining the way data is used with Hazy data — safer, faster and more balanced synthetic data for testing, simulation, machine learning & fintech innovation. Hazy. Hazy is a UCL AI spin out backed by Microsoft and Nationwide. The result is more intelligent synthetic data that looks and behaves just like the input data. 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. It is equivalent to the uncertainty or randomness of a variable. 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. If the events are categorical instead of numeric (for instance medical exams), the same concept still applies but we use Mutual Information instead. Synthetic data enables fast innovation by providing a safe way to share very sensitive data, like banking transactions, without compromising privacy. 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 synthetic data quality metrics explained By Armando Vieira on 15 Jan 2021. 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 synthetic data is already being used at major financial institutions for app developers to simulate realistic client behavior patterns before there are even users. The synthetic data should preserve this temporal pattern as well as replicate the frequency of events, costs, and outcomes. The metrics above give a good understanding of the quality of synthetic 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. In 2018, Hazy won the $1 million Microsoft Innovate.AI prize for the best AI startup in Europe. In other words, the synthetic data keeps all the data value while not compromising any of the privacy. Synthetic data enables fast innovation by providing a safe way to share very sensitive data, like banking transactions, without compromising privacy. We specialise in the financial services data domain. Hazy generates smart synthetic data that's safe to use, allowing companies to innovate with data without using anything sensitive or real-life. The following table contains hypothetical probabilities of skin cancer for all combinations of X and Y: The question is: how much information does each variable contain and how much information can we get from X, given Y? identifiable features are removed or masked) to create brand new hybrid data. Hazy is a synthetic data company. 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. Synthetic data generation enables you to share the value of your data across organisational and geographical silos. We generate synthetic data for training fraud detection and financial risk models. Good synthetic data should have a Mutual Information score of no less than 0.5. 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