Myth #5: Synthetic data is anonymous Personal information can also be contained in synthetic, i.e. However, the algorithm will discard distinctive information associated only with specific users in order to ensure the privacy of individuals. However, in contrast to the permutation method, some connections between the characteristics are preserved. 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. In our example, it is not difficult to identify the specific Alice Smith, age 25, who visited the hospital on 20.3.2019 and to find out that she suffered a heart attack. Therefore, the size of the synthetic population is independent of the size of the source dataset. In other words, the systematically occurring outliers will also be present in the synthetic population because they are of statistical significance. But would it indeed guarantee privacy? 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. 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. Research has demonstrated over and over again that classic anonymization techniques fail in the era of Big Data. Column-wise permutation’s main disadvantage is the loss of all correlations, insights, and relations between columns. Out-of-Place anonymization. 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. Should we forget pseudonymization once and for all? 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. This ongoing trend is here to stay and will be exposing vulnerabilities faster and harder than ever before. With classic anonymization, we imply all methodologies where one manipulates or distorts an original dataset to hinder tracing back individuals. To provide privacy protection, synthetic data is created through a complex process of data anonymization. Synthetic data is used to create artificial datasets instead of altering the original dataset or using it as is and risking privacy and security. With these tools in hand, you will learn how to generate a basic synthetic (fake) data set with the differential privacy guarantee for public data release. Therefore, a typical approach to ensure individuals’ privacy is to remove all PII from the data set. Linkage attacks can have a huge impact on a company’s entire business and reputation. Contact us to learn more. Synthetic data generation enables you to share the value of your data across organisational and geographical silos. Data that is fully anonymized so that an attacker cannot re-identify individuals is not of great value for statistical analysis. 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. Why still use personal data if you can use synthetic data? Most importantly, all research points to the same pattern: new applications uncover new privacy drawbacks in anonymization methods, leading to new techniques and, ultimately, new drawbacks. “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. One example is perturbation, which works by adding systematic noise to data. Typical examples of classic anonymization that we see in practice are generalization, suppression / wiping, pseudonymization and row and column shuffling. That’s why pseudonymized personal data is an easy target for a privacy attack. 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). De-anonymization attacks on geolocated data, re-identified part of the anonymized Netflix movie-ranking data, a British cybersecurity company closed its analytics business. However, even if we choose a high k value, privacy problems occur as soon as the sensitive information becomes homogeneous, i.e., groups have no diversity. 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. Suppose the sensitive information is the same throughout the whole group – in our example, every woman has a heart attack. Why do classic anonymization techniques offer a suboptimal combination between data-utlity and privacy protection?. De-anonymization attacks on geolocated data are not unheard of either. In other words, the flexibility of generating different dataset sizes implies that such a 1:1 link cannot be found. Once both tables are accessible, sensitive personal information is easy to reverse engineer. This breakdown shows synthetic data as a subset of the anonymized data … 63% of the US population is uniquely identifiable, perturbation is just a complementary measure. 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 Generation utilizes machine learning to create a model from the original sensitive data and then generates new fake aka “synthetic” data by resampling from that model. First, we illustrate improved performance on tumor segmentation by leveraging the synthetic images as a form of data augmentation. This is a big misconception and does not result in anonymous data. ... the synthetic data generation method could get inferences that were at least just as close to the original as inferences made from the k-anonymized datasets, though synthetic more often performed better. 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. The authors also proposed a new solution, l-diversity, to protect data from these types of attacks. MOSTLY GENERATE fits the statistical distributions of the real data and generates synthetic data by drawing randomly from the fitted model. Reje, Niklas . Anonymization through Data Synthesis using Generative Adversarial Networks (ADS-GAN). Lookup data can be prepared for, e.g. No matter what criteria we end up using to prevent individuals’ re-identification, there will always be a trade-off between privacy and data value. Then this blog is a must read for you. The Power of Synthetic Data for overcoming Data Scarcity and Privacy Challenges, “By 2024, 60% of the data used for the development of AI and analytics solutions will be synthetically generated”, Manipulated data (through classic ‘anonymization’). First, it defines pseudonymization (also called de-identification by regulators in other countries, including the US). The general idea is that synthetic data consists of new data points and is not simply a modification of an existing data set. 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 creating fully or partially synthetic datasets based on the original data. The EU launched the GDPR (General Data Protection Regulation) in 2018, putting long-planned data protection reforms into action. The topic is still hot: sharing insufficiently anonymized data is getting more and more companies into trouble. 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. Healthcare: Synthetic data enables healthcare data professionals to allow the public use of record data while still maintaining patient confidentiality. At the center of the data privacy scandal, a British cybersecurity company closed its analytics business putting hundreds of jobs at risk and triggering a share price slide. Such high-dimensional personal data is extremely susceptible to privacy attacks, so proper anonymization is of utmost importance. We can assist you with all aspects of the anonymization process: Anonymization techniques - pertubation, generalization or suppressionUnderstand the risks of anonymization, and when to use synthetic data insteadDetail why publicly releasing anonymized data sets is not a… For data analysis and the development of machine learning models, the social security number is not statistically important information in the dataset, and it can be removed completely. In recent years, data breaches have become more frequent. Still, it is possible, and attackers use it with alarming regularity. Syntho develops software to generate an entirely new dataset of fresh data records. Synthetic data preserves the statistical properties of your data without ever exposing a single individual. What are the disadvantages of classic anonymization? In combination with other sources or publicly available information, it is possible to determine which individual the records in the main table belong to. Merely employing classic anonymization techniques doesn’t ensure the privacy of an original dataset. 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). Synthetic data generated by Statice is privacy-preserving synthetic data as it comes with a data protection guarantee and … Unfortunately, the answer is a hard no. ... Ayala-Rivera V., Portillo-Dominguez A.O., Murphy L., Thorpe C. (2016) COCOA: A Synthetic Data Generator for Testing Anonymization Techniques. So what next? Social Media : Facebook is using synthetic data to improve its various networking tools and to fight fake news, online harassment, and political propaganda from foreign governments by detecting bullying language on the platform. Synthetic data: algorithmically manufactures artificial datasets rather than alter the original dataset. When companies use synthetic data as an anonymization method, a balance must be met between utility and the level of privacy protection. 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). Hereby those techniques with corresponding examples. the number of linkage attacks can increase further. No matter if you generate 1,000, 10,000, or 1 million records, the synthetic population will always preserve all the patterns of the real data. As more connected data becomes available, enabled by semantic web technologies, the number of linkage attacks can increase further. Thanks to the privacy guarantees of the Statice data anonymization software, companies generate privacy-preserving synthetic data compliant for any type of data integration, processing, and dissemination. Medical image simulation and synthesis have been studied for a while and are increasingly getting traction in medical imaging community [ 7 ] . Conducting extensive testing of anonymization techniques is critical to assess their robustness and identify the scenarios where they are most suitable. Generalization is another well-known anonymization technique that reduces the granularity of the data representation to preserve privacy. artificially generated, data. Is this true anonymization? So what does it say about privacy-respecting data usage? We do that  with the following illustration with applied suppression and generalization. Although an attacker cannot identify individuals in that particular dataset directly, data may contain quasi-identifiers that could link records to another dataset that the attacker has access to. Synthetic data generation for anonymization purposes. Instead of changing an existing dataset, a deep neural network automatically learns all the structures and patterns in the actual data. We have illustrated the retained distribution in synthetic data using the Berka dataset, an excellent example of behavioral data in the financial domain with over 1 million transactions. Re-identification, in this case, involves a lot of manual searching and the evaluation of possibilities. 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. Others de-anonymized the same dataset by combining it with publicly available Amazon reviews. Most importantly, customers are more conscious of their data privacy needs. In conclusion, from a data-utility and privacy protection perspective, one should always opt for synthetic data when your use-case allows so. Synthetic data. @inproceedings{Heldal2019SyntheticDG, title={Synthetic data generation for anonymization purposes. Data anonymization refers to the method of preserving private or confidential information by deleting or encoding identifiers that link individuals to the stored data. A sign of changing times: anonymization techniques sufficient 10 years ago fail in today’s modern world. For instance, 63% of the US population is uniquely identifiable by combining their gender, date of birth, and zip code alone. Do you still apply this as way to anonymize your dataset? ‘anonymized’ data can never be totally anonymous. In contrast to other approaches, synthetic data doesn’t attempt to protect privacy by merely masking or obfuscating those parts of the original dataset deemed privacy-sensitive while leaving the rest of the original dataset intact. When companies use synthetic data as an anonymization method, a balance must be met between utility and the level of privacy protection. Manipulating a dataset with classic anonymization techniques results in 2 keys disadvantages: We demonstrate those 2 key disadvantages, data utility and privacy protection. Note: we use images for illustrative purposes. Let’s see an example of the resulting statistics of MOSTLY GENERATE’s synthetic data on the Berka dataset. This blogpost will discuss various techniques used to anonymize data. Statistical granularity and data structure is maximally preserved. Synthetic data comes with proven data … This public financial dataset, released by a Czech bank in 1999, provides information on clients, accounts, and transactions. No. Application on the Norwegian Survey on living conditions/EHIS JOHAN HELDAL AND DIANA-CRISTINA IANCU STATISTICS NORWAY, DEPARTMENT OF METHODOLOGY AND DATA COLLECTION JOINT UNECE/EUROSTAT WORK SESSION ON STATISTICAL DATA CONFIDENTIALITY 29-31 OCTOBER 2019, THE HAGUE One of those promising technologies is synthetic data – data that is created by an automated process such that it holds similar statistical patterns as an original dataset. - Provides excellent data anonymization - Can be scaled to any size - Can be sampled from unlimited times. On the other hand, if data anonymization is insufficient, the data will be vulnerable to various attacks, including linkage. The algorithm automatically builds a mathematical model based on state-of-the-art generative deep neural networks with built-in privacy mechanisms. According to Cisco’s research, 84% of respondents indicated that they care about privacy. Nowadays, more people have access to sensitive information, who can inadvertently leak data in a myriad of ways. Keeping these values intact is incompatible with privacy, because a maximum or minimum value is a direct identifier in itself. In contrast to other approaches, synthetic data doesn’t attempt to protect privacy by merely masking or obfuscating those parts of the original dataset deemed privacy-sensitive while leaving the rest of the original dataset intact. 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. Data anonymization, with some caveats, will allow sharing data with trusted parties in accordance with privacy laws. One of the most frequently used techniques is k-anonymity. Once this training is completed, the model leverages the obtained knowledge to generate new synthetic data from scratch. 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. Application on the Norwegian Survey on living conditions/EHIS Johan Heldal and Diana-Cristina Iancu (Statistics Norway) Johan.Heldal@ssb.no, Diana-Cristina.Iancu@ssb.no Abstract and Paper There has been a growing amount of work in recent years on the use of synthetic data as a disclosure control Due to built-in privacy mechanisms, synthetic populations generated by MOSTLY GENERATE can differ in the minimum and maximum values if they only rely on a few individuals. 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. How can we share data without violating privacy? 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. 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. Synthetic Data Generation for Anonymization. We can choose from various well-known techniques such as: We could permute data and change Alice Smith for Jane Brown, waiter, age 25, who came to the hospital on that same day. Synthetic data contains completely fake but realistic information, without any link to real individuals. Synthetic data keeps all the variable statistics such as mean, variance or quantiles. Two new approaches are developed in the context of group anonymization. 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. The disclosure of not fully anonymous data can lead to international scandals and loss of reputation. The final conclusion regarding anonymization: ‘anonymized’ data can never be totally anonymous. This artificially generated data is highly representative, yet completely anonymous. 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. First, we illustrate improved performance on tumor segmentation by leveraging the synthetic images as a form of data augmentation. 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 … The power of big data and its insights come with great responsibility. Nevertheless, even l-diversity isn’t sufficient for preventing attribute disclosure. Since synthetic data contains artificial data records generated by software, personal data is simply not present resulting in a situation with no privacy risks. Randomization is another classic anonymization approach, where the characteristics are modified according to predefined randomized patterns. MOSTLY GENERATE makes this process easily accessible for anyone. This introduces the trade-off between data utility and privacy protection, where classic anonymization techniques always offer a suboptimal combination of both. We can go further than this and permute data in other columns, such as the age column. Information to identify real individuals is simply not present in a synthetic dataset. There are many publicly known linkage attacks. In our example, we can tell how many people suffer heart attacks, but it is impossible to determine those people’s average age after the permutation. 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. 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. However, progress is slow. 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. Accordingly, you will be able to obtain the same results when analyzing the synthetic data as compared to using the original data. It can be described that you have a data set, it is then anonymized, then that anonymized data is converted to synthetic data. Effectively anonymize your sensitive customer data with synthetic data generated by Statice. GDPR’s significance cannot be overstated. Explore the added value of Synthetic Data with us, Software test and development environments. Not all synthetic data is anonymous. 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