Anonymization Tool Free
Data anonymization is a great way of getting more from your data whilst remaining the right side of legal and ethical lines.
Confidentiality: We will not, in any circumstances, store, and share data provided by you with other individuals or organizations. We use the information to process your request and present you with the information you need to access.
Why I need data anonymization?
In today's world data is of utmost importance to an organization for making more informed and powerful data driven business decision. Industries such as banking,financial services, credit card processors, insurance companies, delivery and logistics, telecom operators would benefit greatly by the data anonymization techniques.
“In God we Trust, all others bring data” Edwards Deming
Data is an important asset to the business and business should be highly concerned with their data privacy policies and procedures. Innovative companies highly rely on good quality data for their research and marketing.
Data anonymization is a great way of getting more from your data in a privacy-friendly way remaining the right side of legal and ethical lines.
Information is the oil of the 21st Century, and analytics is the combustion engine.
Senior Vice President and Global Head of Research at Garter, Inc
We didn’t take a broad enough view of our responsibility, and that was a big mistake. It was my mistake, and I’m sorry. I started Facebook, I run it, and I’m responsible for what happens here.
Cosmos Thrace Data Anonymization solution
Cosmos Thrace provides best practice solution driven data anonymization platform that promotes anonymity and data minimization through a number of design decisions. Our anonymization processes are privacy-driven, interactive and enables a very high level of data utility for precise analyses to unlock the power in your data wherever it lives—on premises, in the cloud, and in hybrid environments.
Some of the techniques we use to protect data:
- Suppression - removing of entire columns of data
- Character masking - changing certain characters or values with a chosen repeating symbol (for instance, hiding name "John" with "****" or "xxxx"), advantage of masking is the ability to identify data without manipulating actual identities.
Data masking is a more widely applicable solution as it enables organizations to maintain the usability of their customer data. Using masking, data can be de-identified and de-sensitized so that personal information remains anonymous in the context of support, analytics, testing, or outsourcing.
- Pseudonymisation - replacing values with made up values. To prevent cross-dataset linkage, it is recommended not to use persistent pseudonyms for an individual in more than one dataset, pseudonyms can also be randomly or deterministically generated.
- Swapping - randomly shuffling values in the dataset, so that they do not correspond to the original records.
We are very flexible in our data anonymization solution designs that can easily train design model for a very specific entities depending on the use cases as per the customer’s need and ensure that risk threshold has not been surpassed.
Maintenance and Support
We are highly flexible in our approach to adopt customer requirements and we at CosmosThrace believe in simplified maintenance by changing or detecting new categories of data and training or re-training existing models, instead of having to modify complex regular expressions.
We will provide end-to-end data anonymization solution support as:
1. Load the data
2. Identifying and sensitive/non-sensitive data
3. Transforming the dataset of any size via AI/ML algorithms
4. Process monitoring
5. Analysing the performance issues
6. Execution reporting
7. Continuous improvement of the solution’s performance
8. Cloud based solution or on premise if client does not want to upload data to the cloud
Risks associated with data anonymization
Identity disclosure: Also known as singling out, identity disclosure is the term used to describe situations in which it is possible to identify all or some of the individuals within a dataset.
Attribute disclosure: Attribute disclosure is the ability to determine if a specific individual holds an attribute within a dataset.
Linkability: Linkability refers to when it is possible to connect multiple data points, whether in the same dataset or separate datasets, to create a more cohesive picture of a specific individual.
Inference disclosure: Inference disclosure occurs when you are able to confidently make an inference about the value of an attribute based off other attributes.
Adopting the good practice can help reduce the risk associated with producing – and particularly publishing – anonymised data and will give reasonable degree of confidence that anonymized data will not lead to an inappropriate disclosure of personal data – through 're-identification' or the risk data set being matched with any kind of public records.