For us at Hazy, the most exciting application of synthetic data is when it is combined with anonymised historical data (e.g. The few datasets that are currently considered, both for assessment and training of learning-based dehazing techniques, exclusively rely on synthetic hazy images. 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. Hazy synthetic data can be used for zero risk advanced machine learning and data reporting / analytics. Hazy is the market-leading synthetic data generator. Hazy has 26 repositories available. 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. And synthetic data allows orgs to increase speed to decision making, without risking or getting blocked on real data. "Hazy generates statistically controlled synthetic data that can fix class imbalance, unlock data innovation and help you predict the future. Hazy is a synthetic data generation company. Hazy has 26 repositories available. 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. Synthetic sequential data generation is a challenging problem that has not yet been fully solved. The same for Y = 2 bits, so Y (blood pressure) is more informative about skin cancer than X (blood type). Our synthetic data use cases include: cloud analytics, external analytics, data innovation, data monetisation, and data sourcing. Hazy uses generative models to understand and extract the signal in your data. Synthetic data of good quality should be able to preserve the same order of importance of variables. Hazy synthetic data generation lets you create business insight across company, legal and compliance boundaries — without moving or exposing your 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. We generate synthetic data for training fraud detection and financial risk 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, with Synthetic data enables fast innovation by providing a safe way to share very sensitive data, like banking transactions, without compromising privacy. Hazy generates statistically controlled synthetic data that can fix class imbalance, unlock data innovation and help you predict the future. Where \( \bar{y} \) is the mean of \( y \). Through the testing presented above, we proved that GANs present as an effective way to address this problem. Histogram Similarity is the easiest metric to understand and visualise. It originally span out of UCL just two years ago, but has come a long way since then. With this in mind, Hazy has five major metrics to assess the quality of our synthetic data generation. 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. Hazy Generate scans your raw data and generates a statistically equivalent synthetic version that contains no real information. Hazy synthetic data quality metrics explained By Armando Vieira on 15 Jan 2021. 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. It is equivalent to the uncertainty or randomness of a variable. 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. Armando Vieira is a PhD has a Physics and is being doing Data Science for the last 20 years. Hazy synthetic data generation lets you create business insights across company, legal and compliance boundaries – without moving or exposing your data. Sign up for our sporadic newsletter to keep up to date on synthetic data, privacy matters and machine learning. Learn more about Hazy synthetic data generation and request a demo at Hazy.com. 2 talking about this. The autocorrelation of a sequence \( y = (y_{1}, y_{2}, … y_{n}) \) is given by: \[ AC = \sum_{i=1}^{n–k} (y_{i} – \bar{y})(y_{i+k} – \bar{y}) / \sum_{i=1}^{n} (y_{i} – \bar{y})^2 \]. 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. 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. 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. 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’s synthetic data generation lets you create business insight across company, legal and compliance boundaries — without moving or exposing your data. 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. Hazy. Synthetic data innovation. Read about how we reduced time, cost and risk for Nationwide Building Society by enabling them to generate highly representative synthetic data for transactions. is the entropy, or information, contained in each variable. 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. 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. Our synthetic data use cases include: cloud analytics, external analytics, data innovation, data monetisation, and data sourcing. Sign up for our sporadic newsletter to keep up to date on synthetic data, privacy matters and machine learning. Hazy is a synthetic data company. These models can then be moved safely across company, legal and compliance boundaries. It’s important to our users that they are able to verify the quality of our synthetic data before they use it in production. Good synthetic data should have a Mutual Information score of no less than 0.5. Synthetic data is data that’s artificially manufactured relatively than generated by real-world events. Hazy generates smart synthetic data that's safe to use, allowing companies to innovate with data without using anything sensitive or real-life. For instance, we may use the synthetic data to predict the likelihood of customer churn using, say, an XGBoost algorithm. For us at Hazy, the most exciting application of synthetic data is when it is combined with anonymised historical data (e.g. It originally span out of UCL just two years ago, but has come a long way since then. 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. If the events are categorical instead of numeric (for instance medical exams), the same concept still applies but we use Mutual Information instead. Read about how we reduced time, cost and risk for Nationwide Building Society. Autocorrelation basically measures how events at time \( X(t) \) are related to events at time \( X(t - \delta) \) where \( \delta \) is a lag parameter. When talking about fraud detection, it’s important that seasonality patterns, like weekends and holidays, are preserved. 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. 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. Synthetic data innovation. Hazy generates smart synthetic data that's safe to use, allowing companies to innovate with data without using anything sensitive or real-life. Let’s explore the following example to help explain its meaning. For example, the fintech industry prevents the collection of real user data, as it poses a high risk of fraudulence. “Hazy can help accelerate our work with synthetic datasets,” he … 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. To capture these short and long-range correlations the metric of choice is Autocorrelation with a variable lag parameter. Hazy | 1 429 abonnés sur LinkedIn. 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. Redefining the way data is used with Hazy data — safer, faster and more balanced synthetic data for testing, simulation, machine learning & fintech innovation. Synthetic data sometimes works hand-in-hand with differential privacy, which essentially describes Hazy’s approach. 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(). Author of the book "Business Applications of Deep Learning". Iterate on ideas rapidly. Follow their code on GitHub. This can carry over to machine learning engineers who can better model for this sort of future-demand scenarios. The synthetic data should preserve this temporal pattern as well as replicate the frequency of events, costs, and outcomes. Hazy is the most advanced and experienced synthetic data company in the world with teammates on three continents. Synthetic data enables fast innovation by providing a safe way to share very sensitive data, like banking transactions, without compromising privacy. We are pleased to be cited as having helped improve on their exceptional work. where \(x\) is the original data and \(\hat{x}\) is the synthetic data. This dataset contains records of EEG signals from 120 patients over a series of trials. 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. \]. 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. 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. 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. Synthetic data sometimes works hand-in-hand with differential privacy, which essentially describes Hazy’s approach. \[ H(X) – H(X | Y) = 2 – 11/8 = 0.375bits \]. Advanced generative models that can preserve the relationships in transactional time-series data and real-world customer CIS models. Data science and analytics For us at Hazy, the most exciting application of synthetic data is when it is combined with anonymised historical data (e.g. Hazy – Fraud Detection. 88 percent match for privacy epsilon of 1. 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. Assuming data is tabular, this synthetic data metric quantifies the overlap of original versus synthetic data distributions corresponding to each column. 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. Zero risk, sample based synthetic data generation to safely share your data. Before then being used to generate statistically equivalent synthetic data. 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. Run analytics workloads in the cloud without exposing your data. 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. 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. How can we be sure the synthetic data is really safe and can’t be reverse engineered to disclose private 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. The metrics above give a good understanding of the quality of synthetic data. Hazy is a UCL AI spin out backed by Microsoft and Nationwide. In other words, the synthetic data keeps all the data value while not compromising any of the privacy. This is essential because no customer data is really used, while the curves or patterns of their collective profiles and behaviors are preserved. We work with financial enterprises on reducing the number of false positives in their fraud detection workflow whilst catching the same amount of fraud. 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