Keeping these values intact is incompatible with privacy, because a maximum or minimum value is a direct identifier in itself. A sign of changing times: anonymization techniques sufficient 10 years ago fail in today’s modern world. Synthetic Data Generation for Anonymization. Medical image simulation and synthesis have been studied for a while and are increasingly getting traction in medical imaging community [ 7 ] . A good synthetic data set is based on real connections – how many and how exactly must be carefully considered (as is the case with many other approaches). In reality, perturbation is just a complementary measure that makes it harder for an attacker to retrieve personal data but doesn’t make it impossible. In this course, you will learn to code basic data privacy methods and a differentially private algorithm based on various differentially private properties. Data anonymization, with some caveats, will allow sharing data with trusted parties in accordance with privacy laws. We can go further than this and permute data in other columns, such as the age column. Synthetic data contains completely fake but realistic information, without any link to real individuals. The re-identification process is much more difficult with classic anonymization than in the case of pseudonymization because there is no direct connection between the tables. This artificially generated data is highly representative, yet completely anonymous. This public financial dataset, released by a Czech bank in 1999, provides information on clients, accounts, and transactions. How can we share data without violating privacy? First, it defines pseudonymization (also called de-identification by regulators in other countries, including the US). “In the coming years, we expect the use of synthetic data to really take off.” Anonymization and synthetization techniques can be used to achieve higher data quality and support those use cases when data comes from many sources. This introduces the trade-off between data utility and privacy protection, where classic anonymization techniques always offer a suboptimal combination of both. The final conclusion regarding anonymization: ‘anonymized’ data can never be totally anonymous. Data synthetization is a fundamentally different approach where the source data only serves as training material for an AI algorithm, which learns its patterns and structures. The topic is still hot: sharing insufficiently anonymized data is getting more and more companies into trouble. For example, in a payroll dataset, guaranteeing to keep the true minimum and maximum in the salary field automatically entails disclosing the salary of the highest-paid person on the payroll, who is uniquely identifiable by the mere fact that they have the highest salary in the company. What are the disadvantages of classic anonymization? Healthcare: Synthetic data enables healthcare data professionals to allow the public use of record data while still maintaining patient confidentiality. According to Pentikäinen, synthetic data is a totally new philosophy of putting data together. In other words, k-anonymity preserves privacy by creating groups consisting of k records that are indistinguishable from each other, so that the probability that the person is identified based on the quasi-identifiers is not more than 1/k. data anonymization approaches do not provide rigorous privacy guarantees. One of the most frequently used techniques is k-anonymity. The general idea is that synthetic data consists of new data points and is not simply a modification of an existing data set. The problem comes from delineating PII from non-PII. the number of linkage attacks can increase further. Not all synthetic data is anonymous. Anonymization (strictly speaking “pseudonymization”) is an advanced technique that outputs data with relationships and properties as close to the real thing as possible, obscuring the sensitive parts and working across multiple systems, ensuring consistency. Second, we demonstrate the value of generative models as an anonymization tool, achieving comparable tumor segmentation results when trained on the synthetic data versus when trained on real subject data. - Provides excellent data anonymization - Can be scaled to any size - Can be sampled from unlimited times. The power of big data and its insights come with great responsibility. However, in contrast to the permutation method, some connections between the characteristics are preserved. Application on the Norwegian Survey on living conditions/EHIS}, author={J. Heldal and D. Iancu}, year={2019} } J. Heldal, D. Iancu Published 2019 and Paper There has been a … First, we illustrate improved performance on tumor segmentation by leveraging the synthetic images as a form of data augmentation. Others de-anonymized the same dataset by combining it with publicly available Amazon reviews. In conclusion, from a data-utility and privacy protection perspective, one should always opt for synthetic data when your use-case allows so. The figures below illustrate how closely synthetic data (labeled “synth” in the figures) follows the distributions of the original variables keeping the same data structure as in the target data (labeled “tgt” in the figures). The following table summarizes their re-identification risks and how each method affects the value of raw data: how the statistics of each feature (column in the dataset) and the correlations between features are retained, and what the usability of such data in ML models is. However, progress is slow. Explore the added value of Synthetic Data with us, Software test and development environments. In conclusion, synthetic data is the preferred solution to overcome the typical sub-optimal trade-off between data-utility and privacy-protection, that all classic anonymization techniques offer you. De-anonymization attacks on geolocated data are not unheard of either. Once this training is completed, the model leverages the obtained knowledge to generate new synthetic data from scratch. No. This ongoing trend is here to stay and will be exposing vulnerabilities faster and harder than ever before. It was the first move toward a unified definition of privacy rights across national borders, and the trend it started has been followed worldwide since. Synthetic data by Syntho fills the gaps where classic anonymization techniques fall short by maximizing both data-utility and privacy-protection. Linkage attacks can have a huge impact on a company’s entire business and reputation. Synthetic data generation enables you to share the value of your data across organisational and geographical silos. Out-of-Place anonymization. Synthetic data creating fully or partially synthetic datasets based on the original data. One example is perturbation, which works by adding systematic noise to data. However, with some additional knowledge (additional records collected by the ambulance or information from Alice’s mother, who knows that her daughter Alice, age 25, was hospitalized that day), the data can be reversibly permuted back. Such high-dimensional personal data is extremely susceptible to privacy attacks, so proper anonymization is of utmost importance. Based on GDPR Article 4, Recital 26: “Personal data which have undergone pseudonymisation, which could be attributed to a natural person by the use of additional information should be considered to be information on an identifiable natural person.” Article 4 states very explicitly that the resulting data from pseudonymization is not anonymous but personal data. The EU launched the GDPR (General Data Protection Regulation) in 2018, putting long-planned data protection reforms into action. Hereby those techniques with corresponding examples. In recent years, data breaches have become more frequent. Imagine the following sample of four specific hospital visits, where the social security number (SSN), a typical example of Personally Identifiable Information (PII), is used as a unique personal identifier. Then this blog is a must read for you. However, Product Managers in top-tech companies like Google and Netflix are hesitant to use Synthetic Data because: All anonymized datasets maintain a 1:1 link between each record in the data to one specific person, and these links are the very reason behind the possibility of re-identification. We do that  with the following illustration with applied suppression and generalization. Authorities are also aware of the urgency of data protection and privacy, so the regulations are getting stricter: it is no longer possible to easily use raw data even within companies. Why do classic anonymization techniques offer a suboptimal combination between data-utlity and privacy protection?. Synthetic data has the power to safely and securely utilize big data assets empowering businesses to make better strategic decisions and unlock customer insights confidently. Synthetic data preserves the statistical properties of your data without ever exposing a single individual. Thus, pseudonymized data must fulfill all of the same GDPR requirements that personal data has to. To learn more about the value of behavioral data, read our blog post series describing how MOSTLY GENERATE can unlock behavioral data while preserving all its valuable information. When companies use synthetic data as an anonymization method, a balance must be met between utility and the level of privacy protection. Once the AI model was trained, new statistically representative synthetic data can be generated at any time, but without the individual synthetic data records resembling any individual records of the original dataset too closely. Information to identify real individuals is simply not present in a synthetic dataset. That’s why pseudonymized personal data is an easy target for a privacy attack. ... Ayala-Rivera V., Portillo-Dominguez A.O., Murphy L., Thorpe C. (2016) COCOA: A Synthetic Data Generator for Testing Anonymization Techniques. Conducting extensive testing of anonymization techniques is critical to assess their robustness and identify the scenarios where they are most suitable. Syntho develops software to generate an entirely new dataset of fresh data records. Manipulating a dataset with classic anonymization techniques results in 2 keys disadvantages: We demonstrate those 2 key disadvantages, data utility and privacy protection. Generalization is another well-known anonymization technique that reduces the granularity of the data representation to preserve privacy. Among privacy-active respondents, 48% indicated they already switched companies or providers because of their data policies or data sharing practices. Therefore, a typical approach to ensure individuals’ privacy is to remove all PII from the data set. Data that is fully anonymized so that an attacker cannot re-identify individuals is not of great value for statistical analysis. We have already discussed data-sharing in the era of privacy in the context of the Netflix challenge in our previous blog post. Synthetic data keeps all the variable statistics such as mean, variance or quantiles. In such cases, the data then becomes susceptible to so-called homogeneity attacks described in this paper. But would it indeed guarantee privacy? @inproceedings{Heldal2019SyntheticDG, title={Synthetic data generation for anonymization purposes. Column-wise permutation’s main disadvantage is the loss of all correlations, insights, and relations between columns. Second, we demonstrate the value of generative models as an anonymization tool, achieving comparable tumor segmentation results when trained on the synthetic data versus when trained on real subject data. Two new approaches are developed in the context of group anonymization. In our example, k-anonymity could modify the sample in the following way: By applying k-anonymity, we must choose a k parameter to define a balance between privacy and utility. Since synthetic data contains artificial data records generated by software, personal data is simply not present resulting in a situation with no privacy risks. Is this true anonymization? Producing synthetic data is extremely cost effective when compared to data curation services and the cost of legal battles when data is leaked using traditional methods. It is done to protect the private activity of an individual or a corporation while preserving … First, we illustrate improved performance on tumor segmentation by leveraging the synthetic images as a form of data augmentation. The process involves creating statistical models based on patterns found in the original dataset. Typical examples of classic anonymization that we see in practice are generalization, suppression / wiping, pseudonymization and row and column shuffling. In this case, the values can be randomly adjusted (in our example, by systematically adding or subtracting the same number of days to the date of the visit). Check out our video series to learn more about synthetic data and how it compares to classic anonymization! The pseudonymized version of this dataset still includes direct identifiers, such as the name and the social security number, but in a tokenized form: Replacing PII with an artificial number or code and creating another table that matches this artificial number to the real social security number is an example of pseudonymization. Never assume that adding noise is enough to guarantee privacy! Why still use personal data if you can use synthetic data? Yoon J, Drumright LN, Van Der Schaar M. The medical and machine learning communities are relying on the promise of artificial intelligence (AI) to transform medicine through enabling more accurate decisions and personalized treatment. The same principle holds for structured datasets. Synthetic data is used to create artificial datasets instead of altering the original dataset or using it as is and risking privacy and security. Nevertheless, even l-diversity isn’t sufficient for preventing attribute disclosure. Synthetic data doesn’t suffer from this limitation. This case study demonstrates highlights from our quality report containing various statistics from synthetic data generated through our Syntho Engine in comparison to the original data. Furthermore, GAN trained on a hospital data to generate synthetic images can be used to share the data outside of the institution, to be used as an anonymization tool. 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