Automatic data suppression in SAS reports


protecting-personal-identityHave you ever used SAS to produce reports for publishing? Have you ever thought of or been told about suppressing data in such reports? Why do we need to suppress (in the sense of withholding, concealing, obscuring or hiding) data in published reports?

The reason is simple - in order to protect privacy of individuals - personally identifiable information (PII) - data that could potentially identify specific individuals and their sensitive or confidential information. Such sensitive data can include health insurance and medical records, age, ethnicity, race, gender, education, political or religious believes, financial and credit information, geographical location, criminal history, student education records, etc.

In the U.S., such information is considered confidential and protected by Federal Law, e.g. HIPAA - Health Insurance Portability and Accountability Act and FERPA - Family Educational Rights and Privacy Act. Many other countries have similar laws and regulations.

When SAS is used to process surveys, generate and publish reports, we need to be on a lookout in order not to break the law since demographic component of any survey or report has a potential of breaching privacy protection, especially when we deal with a small group of people. For small reporting samples, even when we publish aggregated reports, there is still a risk of possible deducing or disaggregating personal data.

Grouping for data suppression

One way of obscuring small count numbers to protect people privacy is to lump them up into a larger group, call it “Others” and leave it there. However, while protecting PII this method distorts composition of the report group as it can put different demographic characteristics into “Other” category for different report groups thus making it impossible to compare them side by side.

Using custom formats for data suppression

Another way to suppress or mask small numbers is to use SAS custom formats. Let’s say we want to suppress all numbers in the range of 1 through 6, but show all other numbers as comma-formatted. We can create the following SAS user-defined custom numeric format to suppress small numbers:

proc format;
   value suppress

This works just fine for a single variable (list) frequency or cross-tabulation frequency numbers as long as there are no Total column or Total row presented. If Totals by row or column are reported then the suppressed small number cell can be easily derived from those totals and the values of the other unsuppressed numbers.

Primary and complementary data suppression

When a row or column contains just a single suppressed small number and in addition to frequencies (or counts) we also report totals by rows and/or columns, primary data suppression that is based on the number value itself is not enough for privacy protection as it can be calculated as total minus sum of the other reported numbers in that row or column.

To exclude the possibility of deducing suppressed data, this primary data suppression needs to be complemented by the secondary data suppression when a second small number in that row or column also needs to be suppressed. What further complicates matter is that as long as we suppress that second number in a row, that suppressed cell may become the only suppressed cell across the column. In such a case, it can be derived from the column total and other numbers in that column. To make sure we really protect privacy and eliminate the possibility of unraveling suppressed data in a manner we solve a Sudoku puzzle, we need to complement that secondary suppression with tertiary data suppression of that cell in that column. We will refer to those secondary, tertiary, and so on data suppressions as complementary data suppressions.

Depending on the cardinality of classification variables and data combination, the number of those complementary suppression iterations may vary.

Suppression rules

Unfortunately, there is no unambiguous scientific/statistical definition of “small count numbers” that are subject to suppression to protect personally identifiable information. Such definition of “small count numbers” that need to be suppressed in aggregated reports, suppression rules and suppression protocols vary by jurisdiction (country, state), industry, agency, study, data source, demographic characteristic, effective date, etc. Such definitions can be based on an interval between 1 and a certain threshold (in most cases between 4 and 15). For example, the Environmental Public Health Tracking Network currently suppresses cells based on non-zero counts less than six.

Interestingly, that zero usually is not considered as a “small number” to be suppressed, as zero reveals nobody’s info. Although there might be an interpretation of zeros as not suitable for primary suppression, but to be a fair game for complementary suppression, I don’t think it would make a lot of sense as it would make suppression inconsistent and dependent on the data composition. Besides, suppressing zeros and counting them as complementary may actually aid deducing values of other cells in a row or column that it is supposed to protect. Therefore, we will treat zeros as not to be suppressed neither in primary nor in complementary suppression. The reason is that zeros represent very useful and unique information without compromising any confidentiality, while “small numbers” are not so unique compared to zeros.

Numbers for totals by rows or columns are also subject to suppression by the same measure as the numbers for cross-tabulation cells.

To make our SAS implementation of the small cell numbers suppression as general as possible, we will use a range from 1 through a threshold denoted as &sup_max, where sup_max is a macro variable that can be assigned a value of any natural number.

Automatic suppression algorithm

To fully automate the process of data suppression I developed a simple yet efficient iterative algorithm for implementing small numbers cell suppression using SAS for a two-dimensional tabular report.

  1. Use proc means to calculate count totals and output results in a data table. Class variable values for the totals will have blank values.
  2. Sort the data table from step 1 by class-variable-1, count. That will order counts from smallest to largest within each value of the class-variable-1 (including blanks for totals).
  3. Read through the sorted data table from step 2 by class-variable-1 group and
    • Replace all count values within [1, &sup_max] interval with a special missing value, .S. This is the primary suppression. All primary suppression “suspects” are “eliminated” during this first iteration.
    • Also replace all count variables that are next larger to a suppressed value in a group. This is the secondary complementary suppression.
    • If there are no numbers found to be eligible for primary suppression, the whole suppression process stops after this step without suppressing any data.
  4. Repeat steps 2 and 3 with class-variable-2 variable instead of class-variable-1. This is further complementary suppression.
  5. Keep repeating steps 2 and 3 while flipping class-variable-1 and class-variable-2 until step 3 produces no suppressed value.
  6. Use proc report to output the results with .S values formatted to *.

Suppression implementation with SAS

You can view, download and run the full sample of SAS suppression code.

The following are the code highlights.

  1. Sample data. Since I am creating sample data table within the SAS code itself, I started with a pre-aggregated data table just to cut down on the code size – the code shall work as fine using a non-aggregated input table with one observation per person.
  2. In the Assign macro variables section we assign values for the classification variable names, suppression threshold value ( %let sup_max = 6; ) and global macro variable sup_flag ( %let sup_flag = 1; ). It makes this suppression code implementation ready to be used within a SAS macro or a SAS Stored Process (STP).
  3. Proc format defines formats (notsorted) for class variables with blank value ' ' reserved for ‘Total’. It also defines format cntf for counts and a format $totbgf for background color of the Total column:
    proc format;
       value $racef (notsorted)
          'M' = 'Martian'
          'A' = 'Asian'
          'B' = 'Black'
          'H' = 'Hispanic'
          'W' = 'White'
          ' ' = 'Total'
       value $agegf (notsorted)
          '1' = '<18'
          '2' = '19-64'
          '3' = '65-99'
          '4' = '100+'
          ' ' = 'Total'
       value cntf
          .S = '*'
          . = '0'
          other = [comma18.0]
       value $totbgf 
          'Total' = "&hbcolor"
  4. Proc means with completetypes and preloadfmt options to get all possible combinations of the class variable values:
    proc means data=people_roster noprint completetypes;
       format &tclass1 &f1.. &tclass2 &f2..;
       class &tclass1 &tclass2 / preloadfmt order=data;
       var count;
       output out=people_sup sum(count)=count;
  5. Calculate number of columns in the tabular report (needed for cosmetic purpose to apply distinct color to the background of the Total column):
    /* get number of columns for table report */
    data _null_;
       set people_sup (where=(_type_ eq 1)) end=e;
       totcol + 1; 
       if e then call symput('totcol',strip(put(totcol + 2,best.)));
  6. Suppression macro %suppress parses &dsname table created with proc means above by one of the class variables &supclass and replaces small numbers between 1 and &sup_max with special missing value .S (primary suppression). It also suppresses values next to the primary suppressed value if that value is the only suppressed value in the by group (secondary suppression). In addition, this macro assigns global macro variable &sup_flag to 1 if at least one suppression was done, and to 0 otherwise:
    %macro suppress(dsname=,supclass=);
       proc sort data=&dsname;
          by &supclass count;
       %let sup_flag = 0;
       data &dsname (drop=_supnum_);
          set &dsname;
          by &supclass count;
          if first.&supclass then _supnum_ = 0;
          if (1 le count le &sup_max) or (_supnum_ eq 1 and count ne .S) then
             count = .S;
             call symput('sup_flag','1');
          if (count eq .S) then _supnum_ + 1;
    %mend suppress;
  7. Macro %iterate_suppression to iterate macro %suppress call while it finds new cells to suppress (produces &sup_flag=1). For each iteration, &class1 and &class2 are swapped. This macro is invoked immediately after its definition.
    %macro iterate_supression (class1=, class2=);
       %let nextclass = &class1;
       %do %while (&sup_flag);
          %suppress(dsname=people_sup, supclass=&nextclass);
          %if &nextclass eq &class1
             %then %let nextclass = &class2;
             %else %let nextclass = &class1;
    %mend iterate_supression;
    %iterate_supression (class1=&tclass1, class2=&tclass2);
  8. Conditionally generate footnote if at least one cell is suppressed:
    %let ftnote=;
    data _null_;
       set people_sup (where=(count eq .S));
       call symput('ftnote',"j=L h=10pt '*) The data is suppressed to protect privacy'");
    footnote &ftnote;
  9. Proc report to generate table output. Option preloadfmt preserves the classification variable values order as they are defined by proc format:
    proc report data=people_sup nowd split='*' spanrows missing 
                style(header)=[font_size=11pt just=C color=#&hfcolor]
                style(column)=[font_size=10pt just=L];
       column &tclass1 count,&tclass2 count=tot;
       define &tclass1 / group  order=data "&tlabel1" f=&f1.. preloadfmt style(column)=header[vjust=m cellwidth=1.5in color=#&hfcolor]; 
       define &tclass2 / across order=data "&tlabel2" f=&f2.. preloadfmt style(header)=[background=$totbgf.];;
       define count    / display 'People Reported' style(column)=[just=R cellwidth=1in] f=cntf.;
       define tot      / sum noprint;
       compute &tclass1;
          if &tclass1 eq ' ' then
             call define(_col_,'style',"style=[just=C font_size=11pt color=#&hfcolor]");
             call define(_row_,'style',"style=[font_weight=bold background=#&hbcolor]");
       compute &tclass2;
          call define("_c&totcol._",'style',"style=[font_weight=bold just=R background=#&hbcolor]");

Suppression results

Notice, that all the data suppression manipulations are performed on a single summary data table produced by proc means at the very beginning. Since all the suppression processing is done on a small summary table, it makes its runtime insignificant. In the end, the report has all the small number cells suppressed by both, their values (primary suppression) and the values that complement small numbers in each row and each column (complementary suppression). There is no single suppressed cell in any row or column. This eliminates risk of uniquely deducing suppressed numbers. The following proc report output illustrates the resulting SAS report which is safe to publish:


Behind the curtain

In order to illustrate what is going on during execution of this suppression algorithm, I slightly modified the program to preserve all the interim data (work.people_sup1, work.people_sup2, . . . ) and print interim reports so you can see exactly what is going on at each iteration. You can view, download and run SAS suppression code version that produces interim results. Below are the reports produced at each iteration.

  1. Suppression iteration 1 (searching horizontally; if start searching vertically we still will arrive to the same end result)
    1. Start (here we do primary suppression of numbers between 1 and 6, as well as complementary suppression of second closest number; this iteration takes care of ALL primary suppressions since we search through every cell in a table):sas-report-suppression-iteration-1-start
    2. Finish:
  2. Suppression iteration 2 (searching vertically)
    1. Start:
    2. Finish:
  3. Suppression iteration 3 (searching horizontally)
    1. Start:
    2. Finish:
  4. Suppression iteration 4 (searching vertically)
    1. Start:
    2. Finish (now we have at least two suppressed cell in each column):
  5. Iteration 5 (searching horizontally) did not produced any suppressed cells since we have at least two suppressed cell in each row; flag &sup_flag is set to 0 by macro suppress and the whole suppression process automatically stops.


Your comments and suggestions are welcome as always.


April 6, 2018 Update. SAS code samples for this post have been updated to incorporate newly developed enhanced complementary suppression algorithm. For more information on this topic please refer to the SAS Global Forum 2018 paper Implementing Privacy Protection-Compliant SAS® Aggregate Reports.



About Author

Leonid Batkhan

Leonid Batkhan, Ph.D. in Computer Science and Automatic Control Systems, has been a SAS user for more than 25 years. He came to work for SAS in 1995 and is currently a Senior Consultant with the SAS Federal Data Management and Business Intelligence Practice. During his career, Leonid has successfully implemented dozens of SAS applications and projects in various industries. All posts by Leonid Batkhan >>>


  1. Hi Leonid,
    I am wondering if there is a way to suppress the individual data points for one group of subjects on a scatter plot, while retaining the regression lines?

  2. Thanks Leonid for sharing the algorithm and code. Looking at the total number of lost cells and the number of lost people (9 cells in the given example), one can argue the outcome may not be better than just suppressing the four cells of 4, 6, 8 and 24 in the middle section. I wonder if an objective function can be applied and multile outcomes are compared in order to provide the optimized result.

    • Leonid Batkhan

      Thank you George for your feedback!
      I agree with you that for a particular table composition there might be a number of ways to suppress the cells, and there might be different optimization criteria. The question arises what is the best optimization criteria: to minimize the number of suppressed cells, or suppress the sum of suppressed counts (people), or to minimize the maximum count, or else. Different table compositions may produce totally different results depending on the set criteria. To me this issue is pretty much open...

      • Thanks Leonid, your quick response is much appreciated.
        I was wondering if the optimisation criteria can be treated as a macro parameter so that the user can choose/compare/decide; and I understand this is easy to say but lots of work ...

        • Leonid Batkhan

          Hi George,
          Yes, from the implementation point of view you may have a macro with additional parameter (e.g. METHOD=) that would specify suppression method to be used. Then you would have to supply/code those various suppression methods implementations. One of them is using PROC OPTMODEL as described in the Optimal Cell Suppression in SAS blog post. There is also a high level review of various suppression algorithms (network optimization, linear programming, integer programming, etc.) presented in the Optimization Models and Programs for Cell Suppression in Statistical Tables paper.

          You can run your macro with all the available methods thus generating multiple valid solutions. But still, how do you pick your best solution? What is the best solution? And whatever you pick as the best solution may vary for different table compositions. I see a potential consistency issue if in a single publication one would use several suppressed tables, each produced by a different suppression algorithm.

          • Hi Leonid,
            We don’t have SAS/OR license so PROC OPTMODEL is not an option for me. As to the best solution, my personal view, in order, is to 1) minimize the number of suppressed cells, 2) minimize the sum of suppressed counts (people), and 3) minimize the maximum count (listed in your previous message). Quite often small cell suppressing is done manually in publications and the algorithm is unknown or mixed. Optimized outcome may come from different algorithms for varying tables; I am not sure the consistency is a big issue. The important here is I have learnt your algorithm and your smart code as my starting point. Thank you very much!

  3. Thank you, Leonid, for posting this solution. I ran several times into such requirements generally from EDU customers. And it is great to have direct access to a proven implementation now.

  4. Kristian Lønø on

    This is a interesting solution for a problem we have to deal with at my work, Statistics Norway. I have written a similar program and then I found one special situation where the suppressed numbers are easy to unveil. It is when all the suppressed cells in a row or a column have the values of 1. Then the number of suppressed cells will be the same as the sum of suppressed values which means all the suppressed values in that row or column have to be 1. If we use this infile istead of the one in your example we can easily unveil the suppressed values.

    data people_roster;
    length ageg $1;
    input ageg $ race $ count;
    2 M 1
    2 M 1
    1 A 1
    1 M 1
    1 B 1
    2 A 1
    1 H 7
    2 W 8

    It would be interesting to add a solution to this problem to your program. The program I wrote was longer than yours and became even longer when I took care of this special situation.

    • Leonid Batkhan

      Kristian, it was nice meeting you at SAS Global Forum in Orlando earlier this month. Thank you for taking the lead to set up our meeting to discuss complementary suppression issue that you describe in your comment. That is definitely a flaw in the complementary suppression algorithm implemented and presented in this blog post. I will have to revisit it and address that. I think I will be able to modify the complementary suppression algorithm in a way that can be implemented by adjusting logic in the %suppress macro. I will definitely let you know when I have a chance to implement this improvement. Thank you for your finding and your continued interest in this fascinating topic.

  5. Arlen Harmoning on

    Thanks Leonid. I work with payroll system data that has public, private and confidential components based on State Data Practices and federal laws. I believe that I'll be able to use this more automated process to assist with sensitive reporting areas.

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