Confidentiality
Organizations that collect and disseminate personal and business information face serious privacy challenges. Their statistical information must provide insights into trends, growth, and demographics without disclosing details about individuals or businesses.
In Australia, for example, 59% people are extremely concerned about unauthorized access to or misuse of their personal information. However, the demands are to release greater volumes of data with increasing levels of detail so organizations must find ways not only to improve privacy protection. These pressures, traditionally experienced by National Statistical Offices (NSOs), are now being affecting a wider set of public and private. Web‐based technology and the expectations of informed decision making require safer ways to provide information.
When an organization gathers data ensuring confidentiality is a necessity to maintain the trust of respondents. If a data collector fails to protect privacy, respondents lose trust which leads to reluctance to providing accurate information or any response at all.
The challenge for organizations today is to continue to facilitate users at all levels to experience better informed decision making by providing accurate, appropriate, safe dissemination solutions. They need to do this with fewer resources and in shorter time frames and of course, they need to do it without compromising privacy. Disclosure Control, a practice employed and widely discussed within statistical communities, has a critical role to play in making this possible.
Typically users can be divided into Internal and External users and then further categorized by their user personas. SuperSTAR confidentiality measures can be enforced for any user type, whether it be an internal statistician creating a publication or an external public user accessing data via the Internet.
Secure Access
Only individuals who are verified as a user can view data to which they have been granted specific rights. Groups of users can be created that have similar permissions but are granted specific detailed data access. The groups can be associated to different data access profiles, which ensures a single, consistent repository of data serves all user groups in a secure and suitably controlled manner.
Disclosure Control Methods
There are a variety of methods used to protect data. In some cases, several methods are combined.
- Aggregation – the creation of summary tables or cubes.
- Confidentialization of microdata – sampling and/or perturbing values on data records so that an effectively anonymous set of records can be safely released.
- Confidentialization of tabular data – concealing or adjusting values in aggregate data before release.
- Business rules – controlling the level of detail in queries using pre‐defined rules.
- Trust and access control – providing more detailed access to trusted groups of users.
- Monitoring – recording and reviewing the types of queries executed by individual users and/or groups.
| Read the Confidentiality Solution Paper to find out more about Disclose Control Methods and how the methods are implemented using the Data Control API. |
These methods reduce the amount of information that can be provided to users and each method has a unique set of benefits and limitations. The three most important points to be considered when designing Dissemination Solution involving these methods are:
- Computational complexity, cost, and timeliness.
- Additivity, consistency, and accuracy of subsequent derivations.
- Consequential confidentiality.
| Read the Privacy Protection - Disclosure Control and Confidentiality White Paper to find out more about Disclose Control Methods, their target audience, and the benefits and limitations of each method. |
Dissemination Solutions and Confidentiality
When planning to disseminate statistical information on the Web an organization needs to consider the target audience. The choice of disclosure control method(s) depends on the kind of application used for dissemination as well as the audience.
Given the increasing demand for access to information resources via the Web and the substantial cost advantage of providing end users with self‐service facilities; more organizations are exploring the possibilities of ‘on‐demand’ confidentialization and privacy protection. In an ideal world, robust, automated disclosure control methods would be applied dynamically as information requests were generated from a web‐based, self‐service interface.
| Read the Privacy Protection - Disclosure Control and Confidentiality White Paper to find out more about what Disclosure Control Method is right for the target audience. |
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