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Text of the page (random words):
ion 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 3 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 citation needed data shuffling overcomes reservations about using perturbed or modified confidential data because it retains all the desirable properties of perturbation while performing better than other masking techniques in both data utility and disclosure risk 3 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 it would still prevent traceability back to a known entity based on their known actual date or birth or a known date value for 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 this then defeats the purpose of the masking exercise old databases may then get 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 recognition and a newly acquired interest among the vendors and academia new challenge gave birth to algorithms performing format preserving encryption these are based on the accepted advanced encryption standard aes algorithmic mode recognized by nist 4 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 a 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 at a call centre 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 it is very useful for the billing scenario detailed above it is also commonly known as a dynamic data masking method 5 6 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 7 are a good source of information for exploring some of the more common complex requirements for enterprise masking solutions which include row internal synchronization rules table internal synchronization rules and table 8 to table synchronization rules 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 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 database administrators 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 9 deterministic data masking edit deterministic masking is the process of replacing a value in a column with the same value whether in the same row the same table the same database schema and between instances servers database types example a database has multiple tables each with a column that has first names with deterministic masking the first name will always be replaced with the same value lynne will always become denise wherever lynne may be in the database 10 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 11 and the datasifter method 12 on the fly data masking edit on the fly data masking 13 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 six 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 and database administrators but not for applications because connection pools application caching and data bus hide the application user identity from the database and can also cause application data corruption network proxy between the application and the database captures the sql and applies rewrite on the select request applicable for developers and database administrators with simple select requests but not for stored procedures which the proxy only identifies the exec and applications because connection pools application caching and data bus hide the application user identity from the database and can also cause application data corruption database proxy is a variation of network proxy database proxy is deployed usually between applications users and the database applications and users are connecting to the database through database security proxy there are no changes to the way applications and users are connecting to the database there is also no need of an agent to be installed on the database server the sql queries are rewritten but when implemented this type of dynamic data masking also supported within store procedures and database functions network proxy between the end user and the application identifying text strings and replacing them this method is not applicable for complex applications as it will easily cause corruption when the real time string replacement is unintentionally applied code changes in the applications xacml code changes are usually hard to perform impossible to maintain and not applicable for packaged applications within the application run time by instrumenting the application run time policies are defined to rewrite the result set returned from the data sources while having full visibility to the application user this method is the only applicable way to dynamically mask complex applications as it enables control to the data request data result and user result supported by a browser plugin in the case of saas or local web applications browser add ons can be configured to mask data fields corresponding to precise css selectors this can either be accomplished by marking sensitive fields in the application for example by a html class or by finding the right selectors that identify the fields to be obfuscated or masked data masking and the cloud edit in latest years organizations develop their new applications in the cloud more and more often regardless of whether final applications will be hosted in the cloud or on premises the cloud solutions as of now allow organizations to use infrastructure as a service platform as a service and software as a service there are various modes of creating test data and moving it from on premises databases to the cloud or between different environments within the cloud dynamic data masking becomes even more critical in cloud when customers need to protecting pii data while relying on cloud providers to administer their databases data masking invariably becomes the part of these processes in the systems development life cycle sdlc as the development environments service level agreements slas are usually not as stringent as the production environments slas regardless of whether application is hosted in the cloud or on premises see also edit masking and unmasking by intelligence agencies references edit information management specialists gbt retrieved 24 august 2017 cobb michael what is data masking techniques types and best practices searchsecurity retrieved 2022 11 17 1 2 muralidhar krishnamurty sarathy rathindra 2006 05 01 data shufflinga new masking approach for numerical data management science 52 5 658 670 doi 10 1287 mnsc 1050 0503 issn 0025 1909 data processing systems with format preserving encryption and decryption engines 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 static data masking functions iri retrieved 24 august 2017 deterministic data masking datprof 2020 03 19 retrieved 2020 04 29 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 pmc 6450541 pmid 30962669 eliminating compliance risks data masking in the cloud archived from the original on 4 march 2016 retrieved 24 august 2017 v t e information security threats adware advanced persistent threat arbitrary code execution backdoors bombs fork logic time zip hardware backdoors code injection crimeware cross site scripting cross site leaks dom clobbering history sniffing cryptojacking botnets data breach drive by download browser helper objects viruses data scraping denial of service attack eavesdropping email fraud email spoofing exploits fraudulent dialers hacktivism infostealer insecure direct object reference keystroke loggers malware payload phishing voice polymorphic engine privilege escalation ransomware rootkits scareware shellcode spamming social engineering spyware software bugs trojan horses hardware trojans remote access trojans vulnerability web shells wiper worms sql injection rogue security software zombie vectorial version defenses application security secure coding secure by default secure by design misuse case computer access control authentication multi factor authentication authorization computer security software antivirus software security focused operating system data centric security software obfuscation data masking encryption firewall intrusion detection system host based intrusion detection system hids anomaly detection information security management information risk management security information and event management siem runtime application self protection site isolation scrubber center related security topics computer security automotive security cybercrime cybersex trafficking computer fraud cybergeddon 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