Pdf expert encryption free

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
PDF Expert is one of the most popular PDF editors for Mac. It enables users to annotate, edit, scan, and OCR PDF documents. It is also a PDF. You can also consider option 2, whereby you can use the Open File utility in the window to get the right password encrypted PDF and load it into the program. 4 ways to encrypt or password-protect a PDF for free without Acrobat The world’s most popular PDF reader charges over $20/month to.
 
 

Pdf expert encryption free. How to password protect a PDF

The latter view is great for distraction-free reading. There are tons of keyboard shortcuts you can use that make it easier to move around a PDF file, which you can refer to in the online documentation. You can download SumatraPDF in portable form or install it like a regular program.

It looks extremely similar and works the same. Tons of features are included: Take snapshots of text and images, view the PDF in Read Mode for a more concise reading pane, and have the program read text out loud.

This program works with Windows, Mac, and Linux. You need to manually deselect the offer if you don’t want it. Immediately after opening the mupdf. Once you have, there are literally no options to be seen, but instead the full program window is dedicated to showing the PDF.

Click the top left program icon on MuPDF’s title window, then select About MuPDF to see all the supported shortcut keys you can use to flip through pages, zoom in, and search for text. The other way to use this program is with an initial menu. Open mupdf-gl. You can view bookmarks and a list of pages found in the PDF in an easy to read index on the side of the viewing area.

There are also advanced options like signing and adding text to the PDF. A really valuable search function is included, where the words you search for show up with a bit of context for easier understanding as to where the search terms are at in the text. You can also highlight text, which is great if you’re using a PDF for study notes or a reference document.

The program interface can be a bit nauseating to look at because there are buttons, toolbars, and side panels all over the place. You can easily disable most of these, though, for a much cleaner viewing experience. In addition to opening a PDF from your own local computer, you can also enter a URL of a PDF file the document will still be downloaded, but the program does it for you. You can also add notes, record and attach audio, highlight text, attach files, and add a strike-through to words.

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For instance, patient demographics could be classified as high-risk features. In contrast, lower risk features are those that do not appear in public records or are less readily available. For instance, clinical features, such as blood pressure, or temporal dependencies between events within a hospital e. 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.

Elements of dates that are not permitted for disclosure include the day, month, and any other information that is more specific than the year of an event. Many records contain dates of service or other events that imply age. Ages that are explicitly stated, or implied, as over 89 years old must be recoded as 90 or above.

Dates associated with test measures, such as those derived from a laboratory report, are directly related to a specific individual and relate to the provision of health care. Such dates are protected health information. As a result, no element of a date except as described in 3. This category corresponds to any unique features that are not explicitly enumerated in the Safe Harbor list A-Q , but could be used to identify a particular individual.

Thus, a covered entity must ensure that a data set stripped of the explicitly enumerated identifiers also does not contain any of these unique features. The following are examples of such features:. Identifying Number There are many potential identifying numbers. Identifying Code A code corresponds to a value that is derived from a non-secure encoding mechanism. For instance, a code derived from a secure hash function without a secret key e.

This is because the resulting value would be susceptible to compromise by the recipient of such data. As another example, an increasing quantity of electronic medical record and electronic prescribing systems assign and embed barcodes into patient records and their medications.

See the discussion of re-identification. Identifying Characteristic A characteristic may be anything that distinguishes an individual and allows for identification. Generally, a code or other means of record identification that is derived from PHI would have to be removed from data de-identified following the safe harbor method.

The objective of the paragraph is to permit covered entities to assign certain types of codes or other record identification to the de-identified information so that it may be re-identified by the covered entity at some later date.

In the context of the Safe Harbor method, actual knowledge means clear and direct knowledge that the remaining information could be used, either alone or in combination with other information, to identify an individual who is a subject of the information. This means that a covered entity has actual knowledge if it concludes that the remaining information could be used to identify the individual.

The covered entity, in other words, is aware that the information is not actually de-identified information. Example 2: Clear Familial Relation Imagine a covered entity was aware that the anticipated recipient, a researcher who is an employee of the covered entity, had a family member in the data e. In addition, the covered entity was aware that the data would provide sufficient context for the employee to recognize the relative.

In this situation, the risk of identification is of a nature and degree that the covered entity must have concluded that the recipient could clearly and directly identify the individual in the data.

Example 3: Publicized Clinical Event Rare clinical events may facilitate identification in a clear and direct manner. For instance, imagine the information in a patient record revealed that a patient gave birth to an unusually large number of children at the same time. During the year of this event, it is highly possible that this occurred for only one individual in the hospital and perhaps the country.

As a result, the event was reported in the popular media, and the covered entity was aware of this media exposure. In this case, the risk of identification is of a nature and degree that the covered entity must have concluded that the individual subject of the information could be identified by a recipient of the data.

In this situation, the covered entity has actual knowledge because it was informed outright that the recipient can identify a patient, unless it subsequently received information confirming that the recipient does not in fact have a means to identify a patient.