Varad
SAP Data Anonymization Techniques: Data anonymization can be applied to SQL views by specifying an anonymization method, for example, k-anonymity , l-diversity , or differential privacy and configuring the parameters of the selected data-anonymization method to meet the privacy level required.
SAP Data Anonymization Methods
Data anonymization methods provide a structured approach to modifying data for privacy protection. SAP HANA supports the data anonymization methods k-anonymity, l-diversity, and differential privacy.
Related Information
Differential Privacy
k-Anonymity
k-obscurity is a natural and generally involved technique for changing information for security assurance. It anonymizes information by concealing the singular record in a gathering of comparative records, subsequently fundamentally diminishing the likelihood that the individual can be recognized.
k-obscurity can ordinarily be applied to information that contains straight out values (for instance, ailments), in circumstances where the point is to diminish the gamble of re-distinguishing proof, for instance, involving clinical information for research purposes without distinguishing patients and their singular illnesses and medicines. Despite the fact that k-obscurity doesn’t give formal measurable ensures, it is still generally utilized because of its straightforwardness and instinct, particularly for the anonymization of information containing downright qualities, like clinical records. SAP Data Anonymization
Which fields in the data set contain sensitive, identifying, or quasi-identifying information?
Sensitive information is something that an individual does not want known about them, for example, an illness or a salary. Identifying information is something that directly identifies an individual, such as a name or social security number. Quasi-identifying information may not uniquely identify an individual but if combined with other quasi-identifiers could. SAP Data Anonymization
Sensitive, identifying, and quasi-identifying information
How can quasi-identifying fields be generalized into hierarchical groups?
Removing identifiers is not enough to protect privacy as records can be re-identified by quasi-identifiers. Hierarchical groups are used to make quasi-identifying information in individual records less specific, thus reducing the scope for re-identification.
A hierarchy describes a generalization scheme for the information in a quasi-identifier column. The attributes in each row are replaced by a higher-level group until each group in the data set contains a minimum of k members.
As shown in the examples below, categorical attributes can be grouped into more general categories while numerical attributes can be grouped into ranges or averages.
How many individuals have to be in a crowd for it to be considered anonymous?
This is the value k. A data set is considered k-anonymous if every individual is indistinguishable from at least k – 1 others with respect to the information in the quasi-identifying columns. In the following example, choosing k=2 means at least two rows must have exactly the same combination of quasi-identifying information. If this is not possible, the records are grouped into the next higher-level category. Note that in production the choice k = 2 would not be wise since it offers barely any privacy protection. This value was chosen for illustration purposes only. SAP Data Anonymization
Generalization of categorical attributes along defined hierarchy
You can refine the results of k
-anonymity using additional parameters. How you configure k
-anonymity with these parameters will depend on the data itself, the requirements for your particular scenario, as well as on the applicable data privacy rules and regulations.
l-Diversity
l-diversity can be applied in addition to k-anonymity if there is a risk that too much homogeneity in a sensitive attribute’s values, in combination with other quasi-identifying attributes, might lead to loss of privacy.
For example, suppose that all women in the age group 40-45 and living in a particular district fall within the same income bracket. If you live in that district and you have a female neighbor who is 44, then you can deduce what she earns. The sensitive information has been leaked. SAP Data Anonymization
l-diversity is considered as an addition to k-anonymity. Conversely, k-anonymity can be seen as a special case of l-diversity where l=1.
Using the l-diversity parameter, you can reduce the risk of identification by specifying that a sensitive attribute must have a minimum number of distinct values within each equivalence class. An equivalence class is a set of identical quasi-identifying attributes resulting from k-anonymity. SAP Data Anonymization
The following data set contains identifying, quasi-identifying, and sensitive information:
In this example, age is considered as sensitive personal data that should not be divulged. In other scenarios, age might be treated as a quasi-identifier.
A hierarchy is defined for the location attribute as follows:
If k=2, the result of data anonymization for the different equivalence classes looks like this:
It is possible to deduce the ages of Juliette and Fabienne because in this scenario all women in France are of the same age.
If k=2 and l=2, the rows of the equivalence class with fewer than two distinct values violate the chosen privacy constraint. Where no other parameters have been set, such rows trigger generalization to higher hierarchical levels where larger equivalence classes with at least two distinct values can be formed. In this example, location has been generalized to *:
If a loss parameter is added to the definition, that is, k=2, l=2, loss=0.5, the following happens:
Rows are removed in order to fulfill the other parameter conditions. The data of Juliette and Fabienne is removed from the result altogether.
Differential Privacy
Differential privacy anonymizes data by randomizing sensitive information but in a way that regardless of whether an individual record is included in the data set or not, the outcome of statistical queries remains approximately the same. Differential privacy provides formal statistical privacy guarantees.
The differentially private approach to anonymizing data is typically applied to numerical data in statistical databases. It works by adding noise to the sensitive values to protect privacy, while maximizing the accuracy of queries.
To configure differential privacy, you need to consider the following questions.
What questions do you want the data to answer?
Knowing which queries will be executed on the data determines which columns to include in the data set. For example, to average salaries grouped by gender, region, start year and level, the data table to be anonymized could look like the table below. Direct identifiers and any other unrelated columns are removed.
Original data to be anonymized using differential privacy
How much of an impact can an individual have on the outcome of queries?
The aim of differential privacy is to ensure that regardless of whether an individual record is included in the data or not, a query on the data returns approximately the same result. Therefore, we need to know what the maximum impact of an individual record could be. This will be determined by the highest possible value and the lowest possible value in the data set and is referred to as the sensitivity of the data. The higher the sensitivity, the more noise needs to be applied.
In the above example, the column containing salary information needs to be protected. The maximum impact of an individual value would be the maximum possible salary minus the minimum possible salary. If we know that the maximum possible salary is 80,000 EUR, then the sensitivity value is also 80,000 (maximum salary minus minimum salary, which is 0).
For more technical information about sensitivity, see the section below.
What is the acceptable probability that the outcome of queries changes before it is considered a privacy breach?
This is the value epsilon (ɛ). Typical values are 0.1 or 0.01. However, for some use cases, setting epsilon to a value larger than 1, for example 5, is fine as well. eε is the maximum multiplicative impact on the probability of any outcome. The lower the value of epsilon, the greater the privacy required and the more noise is applied.
For more technical information about epsilon, see the section Differential Privacy – Additional Technical Information below.
Data anonymized using differential privacy
Differential Privacy – Additional Technical Information
The following section provides further technical explanation of differential privacy. This will help you understand how to optimize the utility of anonymized data.
In general, differential privacy is a definition, not a method. If a data set is differentially private, the inclusion or the exclusion of a specific person will only lead to a limited multiplicative impact (eε ) on the probability of each query result on that data set
In the figure below San(DB) represents the differentially private representation of the data set and X the included/excluded person.
Differential Privacy Definition
Differential privacy can be achieved in many ways. SAP HANA uses the Laplace mechanism. It draws noise from a Laplace distribution such that the multiplicative guarantee holds, and requires the definition of the sensitivity, that is the maximum impact an individual can have on the data set with respect to the query results. This depends on the application. For instance, if you want to publish salary data, the minimum possible value one could think of is 0, and the maximum value is the highest possible salary in the company. For other scenarios, defining sensitivity is much easier. For example, consider a data set in which individuals answer a survey in a given range of values.
Choosing the correct sensitivity is necessary to guarantee differential privacy. Setting the sensitivity higher than necessary will reduce the quality of the anonymized data.
Utility Tuning
ε and sensitivity impact the utility of a data set after anonymization. Since ε defines the privacy guarantee and is motivated by external privacy requirements (for example, regulations), it should usually not be changed. However, sensitivity depends on the data set and can be influenced.
Assume you want to publish a data set containing salaries. Usually, there a is a broad range of average salaries and a very low number of very high salaries in the data set. The sensitivity is at least the difference between 0 and the maximum salary (in the data set). However, if you guarantee that no salary larger than a certain upper limit will be in the data set (by filtering the incoming data), you can set the sensitivity much lower. In this way, by filtering and pre-processing the data, you can keep the sensitivity at a certain level and therefore increase the utility of the anonymized data.
Conclusion
In conclusion, SAP Data Anonymization Techniques are indispensable in today’s data-centric landscape. Balancing security, compliance, and performance is challenging but crucial. As technologies evolve, so too must our approaches to safeguarding sensitive information.
FAQs
- Is SAP Data Anonymization mandatory for all businesses? Implementing SAP Data Anonymization is advisable for any business dealing with sensitive information to ensure compliance and data security.
- How often should data anonymization strategies be updated? Regular updates are essential to address evolving data protection regulations and emerging security threats.
- Can SAP Data Anonymization impact system performance? Yes, implementing robust anonymization measures may have a slight impact on system performance, requiring optimization.
- What role does employee training play in data anonymization? Employee training is crucial for creating awareness and ensuring adherence to data anonymization policies, minimizing the risk of human error.
- Are there industry-specific challenges in SAP Data Anonymization? Industries with unique data handling requirements may face specific challenges in implementing effective SAP Data Anonymization.
This article was crafted to provide you with comprehensive insights into SAP Data Anonymization Techniques. If you’re ready to enhance your data security and compliance, explore the possibilities now.
