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On this occasion, professional and Reunion Island Merchants that were either indifferent to or supportive of adware were exposed by affiliates, thus damaging those merchants’ reputations and tarnishing their affiliate marketing efforts. Many affiliates either terminated the use of such merchants or switched to a competitor’s affiliate program. Eventually, affiliate networks were also forced by merchants and affiliates to take a stand and ban certain adware publishers from their network.

Affiliates were among the earliest adopters of pay per click advertising when the first pay-per-click search engines emerged during the end of the s. Later in Google launched its pay per click service, Google AdWords , which is responsible for the widespread use and acceptance of pay per click as an advertising channel.

An increasing number of merchants engaged in pay per click advertising, either directly or via a search marketing agency, and realized that this space was already occupied by their affiliates.

Although this situation alone created advertising channel conflicts and debates between advertisers and affiliates, the largest issue concerned affiliates bidding on advertisers names, brands, and trademarks. Some advertisers, however, did and still do embrace this behavior, going so far as to allow, or even encourage, affiliates to bid on any term, including the advertiser’s trademarks.

Bloggers and other publishers may not be aware of disclosure guidelines set forth by the FTC. Guidelines affect celebrity endorsements, advertising language, and blogger compensation. Affiliate marketing currently lacks industry standards for training and certification.

There are some training courses and seminars that result in certifications; however, the acceptance of such certifications is mostly due to the reputation of the individual or company issuing the certification. Affiliate marketing is not commonly taught in universities, and only a few college instructors work with Internet marketers to introduce the subject to students majoring in marketing.

Education occurs most often in “real life” by becoming involved and learning the details as time progresses. Although there are several books on the topic, some so-called “how-to” or ” silver bullet ” books instruct readers to manipulate holes in the Google algorithm , which can quickly become out of date, [38] or suggest strategies no longer endorsed or permitted by advertisers.

Outsourced Program Management companies typically combine formal and informal training, providing much of their training through group collaboration and brainstorming. Such companies also try to send each marketing employee to the industry conference of their choice. Other training resources used include online forums, weblogs, podcasts , video seminars, and specialty websites.

In the state of New York passed a law asserting sales tax jurisdiction over Amazon. New York was aware of Amazon affiliates operating within the state. In Quill Corp. North Dakota , the US Supreme Court ruled that the presence of independent sales representatives may allow a state to require sales tax collections.

New York determined that affiliates are such independent sales representatives. The New York law became known as “Amazon’s law” and was quickly emulated by other states. Many voucher code web sites use a click-to-reveal format, which requires the web site user to click to reveal the voucher code. The action of clicking places the cookie on the website visitor’s computer. In the United Kingdom, the IAB Affiliate Council under chair Matt Bailey announced regulations [42] that stated that “Affiliates must not use a mechanism whereby users are encouraged to click to interact with content where it is unclear or confusing what the outcome will be.

From Wikipedia, the free encyclopedia. Type of performance-based marketing. For marketing to a specific interest group, see Affinity marketing. This article needs additional citations for verification. Please help improve this article by adding citations to reliable sources. Unsourced material may be challenged and removed. This section possibly contains original research.

Please improve it by verifying the claims made and adding inline citations. Statements consisting only of original research should be removed. February Learn how and when to remove this template message.

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You can keep track of all your in-progress assignments. First, the expert will determine if the demographics are independently replicable.

Features such as birth date and gender are strongly independently replicable—the individual will always have the same birth date — whereas ZIP code of residence is less so because an individual may relocate.

In this case, the expert may determine that public records, such as birth, death, and marriage registries, are the most likely data sources to be leveraged for identification. Third, the expert will determine if the specific information to be disclosed is distinguishable. At this point, the expert may determine that certain combinations of values e. Finally, the expert will determine if the data sources that could be used in the identification process are readily accessible , which may differ by region.

Thus, data shared in the former state may be deemed more risky than data shared in the latter. A qualified expert may apply generally accepted statistical or scientific principles to compute the likelihood that a record in a data set is expected to be unique, or linkable to only one person, within the population to which it is being compared. Figure 4 provides a visualization of this concept. This could occur, for instance, if the data set includes patients over one year-old but the population to which it is compared includes data on people over 18 years old e.

The computation of population uniques can be achieved in numerous ways, such as through the approaches outlined in published literature. Census Bureau to assist in this estimation. In instances when population statistics are unavailable or unknown, the expert may calculate and rely on the statistics derived from the data set.

This is because a record can only be linked between the data set and the population to which it is being compared if it is unique in both. Thus, by relying on the statistics derived from the data set, the expert will make a conservative estimate regarding the uniqueness of records.

Example Scenario Imagine a covered entity has a data set in which there is one 25 year old male from a certain geographic region in the United States. In truth, there are five 25 year old males in the geographic region in question i. Unfortunately, there is no readily available data source to inform an expert about the number of 25 year old males in this geographic region. By inspecting the data set, it is clear to the expert that there is at least one 25 year old male in the population, but the expert does not know if there are more.

So, without any additional knowledge, the expert assumes there are no more, such that the record in the data set is unique. Based on this observation, the expert recommends removing this record from the data set. In doing so, the expert has made a conservative decision with respect to the uniqueness of the record. In the previous example, the expert provided a solution i. In practice, an expert may provide the covered entity with multiple alternative strategies, based on scientific or statistical principles, to mitigate risk.

Figure 4. Relationship between uniques in the data set and the broader population, as well as the degree to which linkage can be achieved. The expert will attempt to determine which record in the data set is the most vulnerable to identification.

However, in certain instances, the expert may not know which particular record to be disclosed will be most vulnerable for identification purposes. In this case, the expert may attempt to compute risk from several different perspectives.

The Privacy Rule does not require a particular approach to mitigate, or reduce to very small, identification risk. The following provides a survey of potential approaches. An expert may find all or only one appropriate for a particular project, or may use another method entirely.

If an expert determines that the risk of identification is greater than very small, the expert may modify the information to mitigate the identification risk to that level, as required by the de-identification standard. In general, the expert will adjust certain features or values in the data to ensure that unique, identifiable elements no longer, or are not expected to, exist. Some of the methods described below have been reviewed by the Federal Committee on Statistical Methodology 16 , which was referenced in the original preamble guidance to the Privacy Rule de-identification standard and recently revised.

Several broad classes of methods can be applied to protect data. An overarching common goal of such approaches is to balance disclosure risk against data utility. However, data utility does not determine when the de-identification standard of the Privacy Rule has been met.

Table 2 illustrates the application of such methods. A first class of identification risk mitigation methods corresponds to suppression techniques. These methods remove or eliminate certain features about the data prior to dissemination.

Suppression of an entire feature may be performed if a substantial quantity of records is considered as too risky e. Suppression may also be performed on individual records, deleting records entirely if they are deemed too risky to share. This can occur when a record is clearly very distinguishing e. Alternatively, suppression of specific values within a record may be performed, such as when a particular value is deemed too risky e. Table 3 illustrates this last type of suppression by showing how specific values of features in Table 2 might be suppressed i.

A second class of methods that can be applied for risk mitigation are based on generalization sometimes referred to as abbreviation of the information. These methods transform data into more abstract representations. Similarly, the age of a patient may be generalized from one- to five-year age groups.

Table 4 illustrates how generalization i. A third class of methods that can be applied for risk mitigation corresponds to perturbation. In this case, specific values are replaced with equally specific, but different, values. Table 5 illustrates how perturbation i. In practice, perturbation is performed to maintain statistical properties about the original data, such as mean or variance. The application of a method from one class does not necessarily preclude the application of a method from another class.

For instance, it is common to apply generalization and suppression to the same data set. Using such methods, the expert will prove that the likelihood an undesirable event e.

For instance, one example of a data protection model that has been applied to health information is the k -anonymity principle. In practice, this correspondence is assessed using the features that could be reasonably applied by a recipient to identify a patient.

Table 6 illustrates an application of generalization and suppression methods to achieve 2-anonymity with respect to the Age, Gender, and ZIP Code columns in Table 2. The first two rows i. Notice that Gender has been suppressed completely i. Table 6, as well as a value of k equal to 2, is meant to serve as a simple example for illustrative purposes only.

Various state and federal agencies define policies regarding small cell counts i. The value for k should be set at a level that is appropriate to mitigate risk of identification by the anticipated recipient of the data set. As can be seen, there are many different disclosure risk reduction techniques that can be applied to health information. However, it should be noted that there is no particular method that is universally the best option for every covered entity and health information set.

Each method has benefits and drawbacks with respect to expected applications of the health information, which will be distinct for each covered entity and each intended recipient. The determination of which method is most appropriate for the information will be assessed by the expert on a case-by-case basis and will be guided by input of the covered entity.

Finally, as noted in the preamble to the Privacy Rule, the expert may also consider the technique of limiting distribution of records through a data use agreement or restricted access agreement in which the recipient agrees to limits on who can use or receive the data, or agrees not to attempt identification of the subjects. Of course, the specific details of such an agreement are left to the discretion of the expert and covered entity.

There has been confusion about what constitutes a code and how it relates to PHI. A common de-identification technique for obscuring PII [Personally Identifiable Information] is to use a one-way cryptographic function, also known as a hash function, on the PII. The Privacy Rule does not limit how a covered entity may disclose information that has been de-identified.

However, a covered entity may require the recipient of de-identified information to enter into a data use agreement to access files with known disclosure risk, such as is required for release of a limited data set under the Privacy Rule. This agreement may contain a number of clauses designed to protect the data, such as prohibiting re-identification.

Further information about data use agreements can be found on the OCR website. R Any other unique identifying number, characteristic, or code, except as permitted by paragraph c of this section; and.

Covered entities may include the first three digits of the ZIP code if, according to the current publicly available data from the Bureau of the Census: 1 The geographic unit formed by combining all ZIP codes with the same three initial digits contains more than 20, people; or 2 the initial three digits of a ZIP code for all such geographic units containing 20, or fewer people is changed to This means that the initial three digits of ZIP codes may be included in de-identified information except when the ZIP codes contain the initial three digits listed in the Table below.

In those cases, the first three digits must be listed as Utilizing Census data, the following three-digit ZCTAs have a population of 20, or fewer persons. To produce a de-identified data set utilizing the safe harbor method, all records with three-digit ZIP codes corresponding to these three-digit ZCTAs must have the ZIP code changed to Covered entities should not, however, rely upon this listing or the one found in the August 14, regulation if more current data has been published.

This new methodology also is briefly described below, as it will likely be of interest to all users of data tabulated by ZIP code. The Census Bureau will not be producing data files containing U.

Zip codes can cross State, place, county, census tract, block group, and census block boundaries. The geographic designations the Census Bureau uses to tabulate data are relatively stable over time.

For instance, census tracts are only defined every ten years. In contrast, ZIP codes can change more frequently.

Postal Service ZIP codes. ZCTAs are generalized area representations of U. The Bureau of the Census provides information regarding population density in the United States.

Covered entities are expected to rely on the most current publicly available Bureau of Census data regarding ZIP codes. The information is derived from the Decennial Census and was last updated in It is expected that the Census Bureau will make data available from the Decennial Census in the near future.

This guidance will be updated when the Census makes new information available. For example, a data set that contained patient initials, or the last four digits of a Social Security number, would not meet the requirement of the Safe Harbor method for de-identification.

 
 

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