Number Masking Softwares
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- Free Masking Software
- Phone Number Masking
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- Data Masking Software Reviews
- Delphi Mask Number Software IP ops v.1.0 IP ops 1.0 is a full-featured program written to help administrators, users and scholars, which needs quick do IP and mask basic calculations.
- Configuring card number masking. Skip to end of metadata. (HKLMSoftware Wow6432Node ITVEventATM for 64-bit). The key value is set as follows: 0 – if no mask to be applied to the card number; 1-6 – number of digits in the beginning of the card number which are not masked.
- Masking Objects with Mask Creator. By gilliandarby 7 Comments. Add impact to elements of your video with video masks. Use brush and shape tools to create and fine-tune a masked area and apply effects such as gray scale, painting, blurring, and more, to selected areas of your video with the new Mask Creator, exclusive to VideoStudio X10 Ultimate.
Data masking or data obfuscation is the process of hiding original data with modified content (characters or other data.)
The main reason for applying masking to a data field is to protect data that is classified as personal identifiable data, personal sensitive data or commercially sensitive data, however the data must remain usable for the purposes of undertaking valid test cycles. It must also look real and appear consistent. It is more common to have masking applied to data that is represented outside of a corporate production system. In other words, where data is needed for the purpose of application development, building program extensions and conducting various test cycles. It is common practice in enterprise computing to take data from the production systems to fill the data component, required for these non-production environments. However, this practice is not always restricted to non-production environments. In some organizations, data that appears on terminal screens to call centre operators may have masking dynamically applied based on user security permissions (e.g. preventing call centre operators from viewing Credit Card Numbers in billing systems).
IANA doesn't directly provide you with an IP address. Instead, they allocate blocks of numbers to different regions. For example, the United States has a reported 1,541,605,760 addresses allocated to it, which is about 36 percent of all the IP addresses available (at least, under IPv4, as opposed to IPv6. The Call Masking functionality of Freshcaller, lets you make calls from your business call center but disguised as a personal number. You can use this functionality to make your business seem personal and local to your customers. Mask your helpline number. Leading Zeros Mask (LZ Mask) In the case where a counting number has more characters than the mask, the counting number wins. For instance, with a mask of 000 and a number of 5348, the result will be 5348 even though the mask has only 3 characters. Data masking or data obfuscation is the process of hiding original data with modified content (characters or other data.). The main reason for applying masking to a data field is to protect data that is classified as personal identifiable data, personal sensitive data or commercially sensitive data, however the data must remain usable for the purposes of undertaking valid test cycles.
The primary concern from a corporate governance perspective is that personnel conducting work in these non-production environments are not always security cleared to operate with the information contained in the production data. This practice represents a security hole where data can be copied by unauthorized personnel and security measures associated with standard production level controls can be easily bypassed. This represents an access point for a data security breach.
The overall practice of Data Masking at an organizational level should be tightly coupled with the Test Management Practice and underlying Methodology and should incorporate processes for the distribution of masked test data subsets.
- 2Techniques
- 3Different types
Background[edit]
Data involved in any. Using this approach we could easily maintain the gender mix within the data structure, apply anonymity to the data records but also maintain a realistic looking database which could not easily be identified as a database consisting of masked data.
This substitution method needs to be applied for many of the fields that are in DB structures across the world, such as telephone numbers, zip codes and postcodes, as well as credit card numbers and other card type numbers like Social Security numbers and Medicare numbers where these numbers actually need to conform to a checksum test of the Luhn algorithm.
In most cases, the substitution files will need to be fairly extensive so having large substitution datasets as well the ability to apply customized data substitution sets should be a key element of the evaluation criteria for any data masking solution.
Shuffling[edit]
The shuffling method is a very common form of data obfuscation. It is similar to the substitution method but it derives the substitution set from the same column of data that is being masked. In very simple terms, the data is randomly shuffled within the column. However, if used in isolation, anyone with any knowledge of the original data can then apply a 'What If' scenario to the data set and then piece back together a real identity. The shuffling method is also open to being reversed if the shuffling algorithm can be deciphered.
Shuffling, however, has some real strengths in certain areas. If for instance, the end of year figures for financial information in a test data base, one can mask the names of the suppliers and then shuffle the value of the accounts throughout the masked database. It is highly unlikely that anyone, even someone with intimate knowledge of the original data could derive a true data record back to its original values.
Number and date variance[edit]
The numeric variance method is very useful for applying to financial and date driven information fields. Effectively, a method utilising this manner of masking can still leave a meaningful range in a financial data set such as payroll. If the variance applied is around +/- 10% then it is still a very meaningful data set in terms of the ranges of salaries that are paid to the recipients.
The same also applies to the date information. If the overall data set needs to retain demographic and actuarial data integrity then applying a random numeric variance of +/- 120 days to date fields would preserve the date distribution but still prevent traceability back to a known entity based on their known actual date or birth or a known date value of whatever record is being masked..
Encryption[edit]
Encryption is often the most complex approach to solving the data masking problem. The encryption algorithm often requires that a 'key' be applied to view the data based on user rights. This often sounds like the best solution but in practice the key may then be given out to personnel without the proper rights to view the data and this then defeats the purpose of the masking exercise. Old databases may then be copied with the original credentials of the supplied key and the same uncontrolled problem lives on.
Recently, the problem of encrypting data while preserving the properties of the entities got a recognition and newly acquired interest among the vendors and academia. New challenge gave birth to algorithms called FPE (format preserving encryption). They are based on the accepted AES algorithmic mode that makes them being recognized by NIST.[1]
Nulling out or deletion[edit]
Sometimes a very simplistic approach to masking is adopted through applying a null value to a particular field. The null value approach is really only useful to prevent visibility of the data element.
In almost all cases it lessens the degree of data integrity that is maintained in the masked data set. It is not a realistic value and will then fail any application logic validation that may have been applied in the front end software that is in the system under test. It also highlights to anyone that wishes to reverse engineer any of the identity data that data masking has been applied to some degree on the data set.
Masking out[edit]
Character scrambling or masking out of certain fields is also another simplistic yet very effective method of preventing sensitive information to be viewed. It is really an extension of the previous method of nulling out but there is greater emphasis on keeping the data real and not fully masked all together.
This is commonly applied to credit card data in production systems. For instance, an operator in a Call Center might bill an item to a customer's credit card. They then quote a billing reference to the card with the last 4 digits of XXXX XXXX xxxx 6789. As an operator they can only see the last 4 digits of the card number, but once the billing system passes the customer's details for charging, the full number is revealed to the payment gateway systems.
This system is not very effective for test systems but is very useful for the billing scenario detailed above. It is also commonly known as a dynamic data masking method.[2][3]
Additional complex rules[edit]
Additional rules can also be factored into any masking solution regardless of how the masking methods are constructed. Product agnostic White Papers[4] are a good source of information for exploring some of the more common complex requirements for enterprise masking solutions which include Row Internal Synchronisation Rules, Table Internal Synchronisation Rules and Table[5] to Table Synchronisation Rules.
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Different types[edit]
Data masking is tightly coupled with building test data. Two major types of data masking are static and on-the-fly data masking.[6]
Static data masking[edit]
Static Data Masking is usually performed on the golden copy of the database, but can also be applied to values in other sources, including files. In DB environments, production DBAs will typically load table backups to a separate environment, reduce the dataset to a subset that holds the data necessary for a particular round of testing (a technique called 'subsetting'), apply data masking rules while data is in stasis, apply necessary code changes from source control, and/or and push data to desired environment.[7]
Statistical data obfuscation[edit]
There are also alternatives to the static data masking that rely on stochastic perturbations of the data that preserve some of the statistical properties of the original data. Examples of statistical data obfuscation methods include differential privacy[8]and the DataSifter method[9].
On-the-fly data masking[edit]
On-the-Fly Data Masking[10] happens in the process of transferring data from environment to environment without data touching the disk on its way. The same technique is applied to 'Dynamic Data Masking' but one record at a time. This type of data masking is most useful for environments that do continuous deployments as well as for heavily integrated applications. Organizations that employ continuous deployment or continuous delivery practices do not have the time necessary to create a backup and load it to the golden copy of the database. Thus, continuously sending smaller subsets (deltas) of masked testing data from production is important. In heavily integrated applications, developers get feeds from other production systems at the very onset of development and masking of these feeds is either overlooked and not budgeted until later, making organizations non-compliant. Having on-the-fly data masking in place becomes essential.
Dynamic data masking[edit]
Dynamic Data Masking is similar to On-the-Fly Data Masking but it differs in the sense that On-the-Fly Data Masking is about copying data from one source to another source so that the latter can be shared. Dynamic data masking happens at runtime, dynamically, and on-demand so that there doesn't need to be a second data source where to store the masked data dynamically.
Dynamic data masking enables several scenarios, many of which revolve around strict privacy regulations e.g. the Singapore Monetary Authority or the Privacy regulations in Europe.
Dynamic data masking is attribute-based and policy-driven. Policies include:
- Doctors can view the medical records of patients they are assigned to (data filtering)
- Doctors cannot view the SSN field inside a medical record (data masking).
Dynamic data masking can also be used to encrypt or decrypt values on the fly especially when using format-preserving encryption.
Several standards have emerged in recent years to implement dynamic data filtering and masking. For instance, XACML policies can be used to mask data inside databases.
There are five possible technologies to apply Dynamic data masking:
- In the Database: Database receives the SQL and applies rewrite to returned masked result set. Applicable for developers & DBAs but not for applications (because connection pools, application caching and. Retrieved 24 August 2017.
- ^'IRI Dynamic Data Masking solutions'. Retrieved 24 August 2017.
- ^'Dynamic Data Masking with IBM Optim'. Retrieved 24 August 2017.
- ^'Data Masking: What You Need to Know'(PDF). Net2000 Ltd. Retrieved 24 August 2017.
- ^'Syncronisation and Complex Data Masking Rules Explained'. Retrieved 24 August 2017.
- ^DataSunrise (2017). 'Dynamic and Static data masking'.
- ^'Static data masking functions'. IRI. Retrieved 24 August 2017.
- ^US 7698250, Cynthia Dwork & Frank McSherry, 'Differential data privacy', published 2010-04-13, assigned to Microsoft Corp (original) and Microsoft Technology Licensing LLC (current)
- ^Marino, Simeone; Zhou, Nina; Zhao, Yi; Zhou, Nina; Wu, Qiucheng; Dinov, Ivo (2018). 'DataSifter: Statistical Obfuscation of Electronic Health Records and Other Sensitive Datasets'. Journal of Statistical Computation and Simulation. 89 (2): 249–271. doi:10.1080/00949655.2018.1545228.
- ^'Eliminating Compliance Risks - Data Masking in the Cloud'. Retrieved 24 August 2017.
In computer science, a mask or bitmask is data that is used for bitwise operations, particularly in a bit field. Using a mask, multiple bits in a byte, nibble, word etc. can be set either on, off or inverted from on to off (or vice versa) in a single bitwise operation.
- 1Common bitmask functions
- 2Uses of bitmasks
Common bitmask functions[edit]
Masking bits to 1[edit]
To turn certain bits on, the bitwise OR operation can be used, following the principle that Y OR 1 = 1 and Y OR 0 = Y. Therefore, to make sure a bit is on, OR can be used with a 1. To leave a bit unchanged, OR is used with a 0.
Example: Masking on the higher nibble (bits 4, 5, 6, 7) the lower nibble (bits 0, 1, 2, 3) unchanged.
Masking bits to 0[edit]
More often in practice bits are 'masked off' (or masked to 0) than 'masked on' (or masked to 1). When a bit is ANDed with a 0, the result is always 0, i.e. Y AND 0 = 0. To leave the other bits as they were originally, they can be ANDed with 1, since Y AND 1 = Y.
Example: Masking off the higher nibble (bits 4, 5, 6, 7) the lower nibble (bits 0, 1, 2, 3) unchanged.
Number Masking Softwares For Pc
Querying the status of a bit[edit]
It is possible to use bitmasks to easily check the state of individual bits regardless of the other bits. To do this, turning off all the other bits using the bitwise AND is done as discussed above and the value is compared with 1. If it is equal to 0, then the bit was off, but if the value is any other value, then the bit was on. What makes this convenient is that it is not necessary to figure out what the value actually is, just that it is not 0.
Example: Querying the status of the 4th bit
Toggling bit values[edit]
So far the article has covered how to turn bits on and turn bits off, but not both at once. Sometimes it does not really matter what the value is, but it must be made the opposite of what it currently is. This can be achieved using the XOR (exclusive or) operation. XOR returns 1if and only if an odd number of bits are 1. Therefore, if two corresponding bits are 1, the result will be a 0, but if only one of them is 1, the result will be 1. Therefore inversion of the values of bits is done by XORing them with a 1. If the original bit was 1, it returns 1 XOR 1 = 0. If the original bit was 0 it returns 0 XOR 1 = 1. Also note that XOR masking is bit-safe, meaning that it will not affect unmasked bits because Y XOR 0 = Y, just like an OR.
Example: Toggling bit values
To write arbitrary 1s and 0s to a subset of bits, first write 0s to that subset, then set the high bits:
Uses of bitmasks[edit]
Arguments to functions[edit]
In programming languages such as C, bit fields are a useful way to pass a set of named boolean arguments to a function. For example, in the graphics API OpenGL, there is a command, glClear() which clears the screen or other buffers. It can clear up to four buffers (the color, depth, accumulation, and stencil buffers), so the API authors could have had it take four arguments. But then a call to it would look like
which is not very descriptive. Instead there are four defined field bits, GL_COLOR_BUFFER_BIT, GL_DEPTH_BUFFER_BIT, GL_ACCUM_BUFFER_BIT, and GL_STENCIL_BUFFER_BIT and glClear() is declared as
Then a call to the function looks like this
Internally, a function taking a bitfield like this can use binary and to extract the individual bits. For example, an implementation of glClear() might look like:
The advantage to this approach is that function argument overhead is decreased. Since the minimum datum size is one byte, separating the options into separate arguments would be wasting seven bits per argument and would occupy more stack space. Instead, functions typically accept one or more 32-bit integers, with up to 32 option bits in each. While elegant, in the simplest implementation this solution is not type-safe. A GLbitfield is simply defined to be an unsigned int, so the compiler would allow a meaningless call to glClear(42) or even glClear(GL_POINTS). In C++ an alternative would be to create a class to encapsulate the set of arguments that glClear could accept and could be cleanly encapsulated in a library (see the external links for an example).
Free Masking Software
Inverse masks[edit]
Masks are used with IP addresses in IP ACLs (Access Control Lists) to specify what should be permitted and denied. To configure IP addresses on interfaces, masks start with 255 and have the large values on the left side: for example, IP address 209.165.202.129 with a 255.255.255.224 mask. Masks for IP ACLs are the reverse: for example, mask 0.0.0.255. This is sometimes called an inverse mask or a wildcard mask. When the value of the mask is broken down into binary (0s and 1s), the results determine which address bits are to be considered in processing the traffic. A 0 indicates that the address bits must be considered (exact match); a 1 in the mask is a 'don't care'. This table further explains the concept.
Mask example:
network address (traffic that is to be processed) 10.1.1.0
mask 0.0.0.255
network address (binary) 00001010.00000001.00000001.00000000
mask (binary) 00000000.00000000.00000000.11111111
Based on the binary mask, it can be seen that the first three sets (octets) must match the given binary network address exactly (00001010.00000001.00000001). The last set of numbers is made of 'don't cares' (.11111111). Therefore, all traffic that begins with 10.1.1. matches since the last octet is 'don't care'. Therefore, with this mask, network addresses 10.1.1.1 through 10.1.1.255 (10.1.1.x) are processed.
Subtract the normal mask from 255.255.255.255 in order to determine the ACL inverse mask. In this example, the inverse mask is determined for network address 172.16.1.0 with a normal mask of 255.255.255.0.
255.255.255.255 - 255.255.255.0 (normal mask) = 0.0.0.255 (inverse mask)
ACL equivalents
The source/source-wildcard of 0.0.0.0/255.255.255.255 means 'any'.
The source/wildcard of 10.1.1.2/0.0.0.0 is the same as 'host 10.1.1.2'
Image masks[edit]
In computer graphics, when a given image is intended to be placed over a background, the transparent areas can be specified through a binary mask. This way, for each intended image there are actually two bitmaps: the actual image, in which the unused areas are given a pixel value with all bits set to 0s, and an additional mask, in which the correspondent image areas are given a pixel value of all bits set to 0s and the surrounding areas a value of all bits set to 1s. In the sample at right, black pixels have the all-zero bits and white pixels have the all-one bits.
At run time, to put the image on the screen over the background, the program first masks the screen pixel's bits with the image mask at the desired coordinates using the bitwise AND operation. This preserves the background pixels of the transparent areas while resets with zeros the bits of the pixels which will be obscured by the overlapped image.
Then, the program renders the image pixel's bits by combining them with the background pixel's bits using the bitwise OR operation. This way, the image pixels are appropriately placed while keeping the background surrounding pixels preserved. The result is a perfect compound of the image over the background.
Phone Number Masking
This technique is used for painting pointing device cursors, in typical 2-D videogames for characters, bullets and so on (the sprites), for GUIicons, and for video titling and other image mixing applications.
Although related (due to being used for the same purposes), transparent colors and alpha channels are techniques which do not involve the image pixel mixage by binary masking.
Hash tables[edit]
To create a hashing function for a hash table, often a function is used that has a large domain. To create an index from the output of the function, a modulo can be taken to reduce the size of the domain to match the size of the array; however, it is often faster on many processors to restrict the size of the hash table to powers of two sizes and use a bitmask instead.
An example of both modulo and masking in C: