Best Data Masking Tools and Software in 2019

 

5. IRI FieldShield Data Masking

Features :

  • IRI FieldShield can automatically find sensitive data at source and classify it. It can then encrypt, pseudonymize or redact the sensitive data while preserving its referential integrity.
  • It can mask data from various structured data sources like databases and flat files.
  • IRI FieldShield de-identifies sensitive data and helps in compliances like HIPAA, PCI, GDPR, CIPSEA, DPA, FERPA, GLBA, POPI, etc.
  • It can help to protect data in integrated development environment (IDE).
  • It can also help to mask big data using existing systems without Hadoop, in-memory databases, or appliances.
  • IRI FieldShield can be used for both Static Data Masking and Dynamic Data Masking.
  • It supports Format Preserving Encryption, so that encrypted and masked data looks more real (What is Format Preserving Encryption ?).
  • It also supports cross-table masking rules, hashing and tokenization and XML audit log file.
  • IRI FieldShield supports field-level protections. If there is a breach and a column with a specific decryption key is compromised, the other columns may still be protected.
Price : Please contact IRI sales for information on pricing.

 

6. CA Test Data Manager

Features :

  • Using CA Test Data Manager, one can generate real looking test data without compromising privacy of data, thus helping in complying with privacy regulations.
  • It can identify Personally Identifiable Information across multiple data sources and display them as per severity level. Test Data Engineers and Compiance Officers can then review and tag the data for further processing.
  • CA Test Data Manager can generate the smallest set of data needed for comprehensive testing.
  • It also helps in creating virtual copies of test data on-demand and can give each tester his own virtual copy of test data with no storage overhead.
Price : Please contact CA Technologies sales for information on pricing.

 

7. Net2000 Data Masker

Features :

  • Using Net2000 one can substitute, scramble or otherwise obfuscate data in non-production database to create real looking test data.
  • Data Masker usually can mask data with complex data structures. There is a list of pre-defined datasets. But, one can also create user defined datasets and mask it.
  • While masking data in database, it can preserve data relationships between rows in tables, between rows in the same table and internally between columns in the same row.
  • It can also handle data synchronization issues automatically by the addition of configurable masking rules.
  • The Data Masker software is installed on a Windows PC and can operate on both local and remote databases. There are no server side components for Data Masker. It can support Oracle versions 9i, 10g, 11g, 12c, AWS RDS for Oracle and Sql Server versions 2005, 2008, 2012, 2014 and 2016 as well as Azure SQL Database and AWS RDS SQL Server.
Price : Please contact Net2000 (now acquired by Redgate Software) sales for information on pricing.

Calculate the pseudoinverse of a matrix using Python

What is the pseudoinverse of a matrix? We know that if A is a square matrix with full rank, then A-1 is said to be the inverse of A if the following condition holds: $latex AA^{-1}=A^{-1}A=I $ The pseudoinverse or the Moore-Penrose inverse of a matrix is a...

Cholesky decomposition using Python

What is Cholesky decomposition? A square matrix A is said to have Cholesky decomposition if it can be written as a product of a lower triangular matrix and its conjugate transpose. $latex A=LL^{*} $ If all the entries of A are real numbers, then the conjugate...

Tensor Hadamard Product using Python

In one of our previous articles, we already discussed what the Hadamard product in linear algebra is. We discussed that if A and B are two matrices of size mxn, then the Hadamard product of A and B is another mxn matrix C such that: $latex H_{i,j}=A_{i,j} \times...

Perform tensor addition and subtraction using Python

We can use numpy nd-array to create a tensor in Python. We can use the following Python code to perform tensor addition and subtraction. import numpy A = numpy.random.randint(low=1, high=10, size=(3, 3, 3)) B = numpy.random.randint(low=1, high=10, size=(3, 3, 3)) C =...

How to create a tensor using Python?

What is a tensor? A tensor is a generalization of vectors and matrices. It is easily understood as a multidimensional array. For example, in machine learning, we can organize data in an m-way array and refer it as a data tensor. Data related to images, sounds, movies,...

How to combine NumPy arrays using horizontal stack?

We can use the hstack() function from the numpy module to combine two or more NumPy arrays horizontally. For example, we can use the following Python code to combine three NumPy arrays horizontally. import numpy A = numpy.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) B =...

How to combine NumPy arrays using vertical stack?

Let’s say we have two or more NumPy arrays. We can combine these NumPy arrays vertically using the vstack() function from the numpy module. For example, we can use the following Python code to combine three NumPy arrays vertically. import numpy A = numpy.array([[1, 2,...

Singular Value Decomposition (SVD) using Python

What is Singular Value Decomposition (SVD)? Let A be an mxn rectangular matrix. Using Singular Value Decomposition (SVD), we can decompose the matrix A in the following way: $latex A_{m \times n}=U_{m \times m}S_{m \times n}V_{n \times n}^T $ Here, U is an mxm matrix....

Eigen decomposition of a square matrix using Python

Let A be a square matrix. Let’s say A has k eigenvalues λ1, λ2, ... λk. And the corresponding eigenvectors are X1, X2, ... Xk. $latex X_1=\begin{bmatrix} x_{11} \\ x_{21} \\ x_{31} \\ ... \\ x_{k1} \end{bmatrix} \\ X_2=\begin{bmatrix} x_{12} \\ x_{22} \\ x_{32} \\ ......

How to calculate eigenvalues and eigenvectors using Python?

In our previous article, we discussed what eigen values and eigenvectors of a square matrix are and how we can calculate the eigenvalues and eigenvectors of a square matrix mathematically. We discussed that if A is a square matrix, then $latex (A- \lambda I) \vec{u}=0...

Not a premium member yet?

Please follow the link below to buy The Security Buddy Premium Membership.

Featured Posts

Translate »