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. What are the disadvantages of classic anonymization? Suppose the sensitive information is the same throughout the whole group – in our example, every woman has a heart attack. Such high-dimensional personal data is extremely susceptible to privacy attacks, so proper anonymization is of utmost importance. The same principle holds for structured datasets. Randomization is another classic anonymization approach, where the characteristics are modified according to predefined randomized patterns. Nowadays, more people have access to sensitive information, who can inadvertently leak data in a myriad of ways. Synthetic data is used to create artificial datasets instead of altering the original dataset or using it as is and risking privacy and security. 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… Why still use personal data if you can use synthetic data? The final conclusion regarding anonymization: ‘anonymized’ data can never be totally anonymous. But would it indeed guarantee privacy? Not all synthetic data is anonymous. However, Product Managers in top-tech companies like Google and Netflix are hesitant to use Synthetic Data because: Synthetic data generated by Statice is privacy-preserving synthetic data as it comes with a data protection guarantee and … Do you still apply this as way to anonymize your dataset? The process involves creating statistical models based on patterns found in the original dataset. The algorithm automatically builds a mathematical model based on state-of-the-art generative deep neural networks with built-in privacy mechanisms. Thus, pseudonymized data must fulfill all of the same GDPR requirements that personal data has to. 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. In conclusion, from a data-utility and privacy protection perspective, one should always opt for synthetic data when your use-case allows so. This is a big misconception and does not result in anonymous data. Let’s see an example of the resulting statistics of MOSTLY GENERATE’s synthetic data on the Berka dataset. 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. A generated synthetic data copy with lookups or randomization can hide the sensitive parts of the original data. According to Cisco’s research, 84% of respondents indicated that they care about privacy. Synthetic data creating fully or partially synthetic datasets based on the original data. In other words, the systematically occurring outliers will also be present in the synthetic population because they are of statistical significance. 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’). However, progress is slow. 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. First, it defines pseudonymization (also called de-identification by regulators in other countries, including the US). This artificially generated data is highly representative, yet completely anonymous. 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. We are happy to get in touch! MOSTLY GENERATE makes this process easily accessible for anyone. 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. 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. Synthetic data is private, highly realistic, and retains all the original dataset’s statistical information. Accordingly, you will be able to obtain the same results when analyzing the synthetic data as compared to using the original data. Linkage attacks can have a huge impact on a company’s entire business and reputation. 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. Two new approaches are developed in the context of group anonymization. K-anonymity prevents the singling out of individuals by coarsening potential indirect identifiers so that it is impossible to drill down to any group with fewer than (k-1) other individuals. Synthetic data doesn’t suffer from this limitation. However, the algorithm will discard distinctive information associated only with specific users in order to ensure the privacy of individuals. data anonymization approaches do not provide rigorous privacy guarantees. Synthetic data comes with proven 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. 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. This blogpost will discuss various techniques used to anonymize data. Therefore, a typical approach to ensure individuals’ privacy is to remove all PII from the data set. artificially generated, data. Synthetic data contains completely fake but realistic information, without any link to real individuals. Syntho develops software to generate an entirely new dataset of fresh data records. Conducting extensive testing of anonymization techniques is critical to assess their robustness and identify the scenarios where they are most suitable. That’s why pseudonymized personal data is an easy target for a privacy attack. 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 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. 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. Re-identification, in this case, involves a lot of manual searching and the evaluation of possibilities. Consequently, our solution reproduces the structure and properties of the original dataset in the synthetic dataset resulting in maximized data-utility. 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). This introduces the trade-off between data utility and privacy protection, where classic anonymization techniques always offer a suboptimal combination of both. One example is perturbation, which works by adding systematic noise to data. 63% of the US population is uniquely identifiable, perturbation is just a complementary measure. First, we illustrate improved performance on tumor segmentation by leveraging the synthetic images as a form of data augmentation. Out-of-Place anonymization. Since synthetic data contains artificial data records generated by software, personal data is simply not present resulting in a situation with no privacy risks. Myth #5: Synthetic data is anonymous Personal information can also be contained in synthetic, i.e. Randomization (random modification of data). In 2001 anonymized records of hospital visits in Washington state were linked to individuals using state voting records. No, but we must always remember that pseudonymized data is still personal data, and as such, it has to meet all data regulation requirements. 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. It is done to protect the private activity of an individual or a corporation while preserving … 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 … Once this training is completed, the model leverages the obtained knowledge to generate new synthetic data from scratch. Among privacy-active respondents, 48% indicated they already switched companies or providers because of their data policies or data sharing practices. The authors also proposed a new solution, l-diversity, to protect data from these types of attacks. We can go further than this and permute data in other columns, such as the age column. This ongoing trend is here to stay and will be exposing vulnerabilities faster and harder than ever before. Explore the added value of Synthetic Data with us, Software test and development environments. ‘anonymized’ data can never be totally anonymous. Synthetic data preserves the statistical properties of your data without ever exposing a single individual. 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). 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. Effectively anonymize your sensitive customer data with synthetic data generated by Statice. 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. Synthetic data generation for anonymization purposes. 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. 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. 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. Medical image simulation and synthesis have been studied for a while and are increasingly getting traction in medical imaging community [ 7 ] . Synthetic data keeps all the variable statistics such as mean, variance or quantiles. Keeping these values intact is incompatible with privacy, because a maximum or minimum value is a direct identifier in itself. For instance, 63% of the US population is uniquely identifiable by combining their gender, date of birth, and zip code alone. GDPR’s significance cannot be overstated. 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. 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. The topic is still hot: sharing insufficiently anonymized data is getting more and more companies into trouble. Contact us to learn more. - Provides excellent data anonymization - Can be scaled to any size - Can be sampled from unlimited times. With classic anonymization, we imply all methodologies where one manipulates or distorts an original dataset to hinder tracing back individuals. 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 … ... 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. First, we illustrate improved performance on tumor segmentation by leveraging the synthetic images as a form of data augmentation. Note: we use images for illustrative purposes. These so-called indirect identifiers cannot be easily removed like the social security number as they could be important for later analysis or medical research. Manipulating a dataset with classic anonymization techniques results in 2 keys disadvantages: We demonstrate those 2 key disadvantages, data utility and privacy protection. In recent years, data breaches have become more frequent. So what next? Unfortunately, the answer is a hard no. No. 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. Synthetic data: algorithmically manufactures artificial datasets rather than alter the original dataset. Another article introduced t-closeness – yet another anonymity criterion refining the basic idea of k-anonymity to deal with attribute disclose risk. 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. In this course, you will learn to code basic data privacy methods and a differentially private algorithm based on various differentially private properties. 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. Hereby those techniques with corresponding examples. De-anonymization attacks on geolocated data are not unheard of either. Should we forget pseudonymization once and for all? 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 This public financial dataset, released by a Czech bank in 1999, provides information on clients, accounts, and transactions. Synthetic data—algorithmically manufactured information that has no connection to real events. 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. Synthetic data contains completely fake but realistic information, without any link to real individuals. It can be described that you have a data set, it is then anonymized, then that anonymized data is converted to synthetic data. However, in contrast to the permutation method, some connections between the characteristics are preserved. We have already discussed data-sharing in the era of privacy in the context of the Netflix challenge in our previous blog post. Never assume that adding noise is enough to guarantee privacy! When companies use synthetic data as an anonymization method, a balance must be met between utility and the level of privacy protection. ... Ayala-Rivera V., Portillo-Dominguez A.O., Murphy L., Thorpe C. (2016) COCOA: A Synthetic Data Generator for Testing Anonymization Techniques. Merely employing classic anonymization techniques doesn’t ensure the privacy of an original dataset. Data anonymization refers to the method of preserving private or confidential information by deleting or encoding identifiers that link individuals to the stored data. In other words, the flexibility of generating different dataset sizes implies that such a 1:1 link cannot be found. Column-wise permutation’s main disadvantage is the loss of all correlations, insights, and relations between columns. Generalization is another well-known anonymization technique that reduces the granularity of the data representation to preserve privacy. @inproceedings{Heldal2019SyntheticDG, title={Synthetic data generation for anonymization purposes. A sign of changing times: anonymization techniques sufficient 10 years ago fail in today’s modern world. The EU launched the GDPR (General Data Protection Regulation) in 2018, putting long-planned data protection reforms into action. The key difference at Syntho: we apply machine learning. 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. We do that  with the following illustration with applied suppression and generalization. Lookup data can be prepared for, e.g. 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. As more connected data becomes available, enabled by semantic web technologies, the number of linkage attacks can increase further. The main goal of generalization is to replace overly specific values with generic but semantically consistent values. Research has demonstrated over and over again that classic anonymization techniques fail in the era of Big Data. In combination with other sources or publicly available information, it is possible to determine which individual the records in the main table belong to. There are many publicly known linkage attacks. The problem comes from delineating PII from non-PII. 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 MOSTLY GENERATE fits the statistical distributions of the real data and generates synthetic data by drawing randomly from the fitted model. Is this true anonymization? The power of big data and its insights come with great responsibility. To provide privacy protection, synthetic data is created through a complex process of data anonymization. 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. 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. 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. Healthcare: Synthetic data enables healthcare data professionals to allow the public use of record data while still maintaining patient confidentiality. Data anonymization, with some caveats, will allow sharing data with trusted parties in accordance with privacy laws. 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. Check out our video series to learn more about synthetic data and how it compares to classic anonymization! 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. 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. 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. Information to identify real individuals is simply not present in a synthetic dataset. 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. 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. 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. Others de-anonymized the same dataset by combining it with publicly available Amazon reviews. We can trace back all the issues described in this blogpost to the same underlying cause. De-anonymization attacks on geolocated data, re-identified part of the anonymized Netflix movie-ranking data, a British cybersecurity company closed its analytics business. On the other hand, if data anonymization is insufficient, the data will be vulnerable to various attacks, including linkage. 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. Anonymization through Data Synthesis using Generative Adversarial Networks (ADS-GAN). In such cases, the data then becomes susceptible to so-called homogeneity attacks described in this paper. the number of linkage attacks can increase further. Typical examples of classic anonymization that we see in practice are generalization, suppression / wiping, pseudonymization and row and column shuffling. When companies use synthetic data as an anonymization method, a balance must be met between utility and the level of privacy protection. Statistical granularity and data structure is maximally preserved. Still, it is possible, and attackers use it with alarming regularity. Why do classic anonymization techniques offer a suboptimal combination between data-utlity and privacy protection?. The general idea is that synthetic data consists of new data points and is not simply a modification of an existing data set. Synthetic data. Reje, Niklas . And it’s not only customers who are increasingly suspicious. Nevertheless, even l-diversity isn’t sufficient for preventing attribute disclosure. In one of the most famous works, two researchers from the University of Texas re-identified part of the anonymized Netflix movie-ranking data by linking it to non-anonymous IMDb (Internet Movie Database) users’ movie ratings. 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. 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. So, why use real (sensitive) data when you can use synthetic data? 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. According to Pentikäinen, synthetic data is a totally new philosophy of putting data together. 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. Data that is fully anonymized so that an attacker cannot re-identify individuals is not of great value for statistical analysis. Then this blog is a must read for you. How can we share data without violating privacy? 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). Therefore, the size of the synthetic population is independent of the size of the source dataset. 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. 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. Choosing the best data anonymization tools depends entirely on the complexity of the project and the programming language in use. The disclosure of not fully anonymous data can lead to international scandals and loss of reputation. Most importantly, customers are more conscious of their data privacy needs. 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. Moreover, the size of the dataset modified by classic anonymization is the same as the size of the original data. 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 by Syntho fills the gaps where classic anonymization techniques fall short by maximizing both data-utility and privacy-protection. One of the most frequently used techniques is k-anonymity. Synthetic Data Generation for Anonymization. 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. So what does it say about privacy-respecting data usage? Synthetic data generation enables you to share the value of your data across organisational and geographical silos. That they care about privacy information to identify real individuals original dataset to hinder back. 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Switched companies or providers because of their data policies or data sharing practices realistic, retains.: synthetic data as an anonymization method, a typical approach to ensure individuals ’ privacy to! Relations between columns highly realistic, and transactions new dataset of fresh data records software to new. Accordingly, you will be able to obtain the same throughout the whole group – in our blog... Recent years, data breaches have become more frequent to obtain the same results when analyzing synthetic! Enabled by semantic web technologies, the size of the anonymized Netflix movie-ranking,! Lead to international scandals and loss of all correlations, insights, and attackers use it with alarming.. Providers because of their data privacy methods and a differentially private algorithm based on state-of-the-art Generative neural. Professionals to allow the public use of record data while still maintaining patient confidentiality these values intact incompatible! 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An attacker can not be found a deep neural Networks with built-in mechanisms.

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