Secondary Cell Suppression on OLAP

I recently had the great pleasure of working with the Department of Health and Human Services in New Hampshire. They have employed a company called Abacus Service Corporation to develop a wonderful dashboarding system in order to assist them in their goals of greater transparency (which I hope to review on here eventually, it’s a combination of Metric Studio and Workspace that is significantly easier for end users). Their team is extremely skilled, and they only needed a few days of my time to get them past a few difficult problems. They’ve agreed to let me publish the solution two of those problems.

One of their goals is to publish reports, directly to the public, on various health related issues. While transparency is to be commended, smaller data sets present possible privacy law violations. If data was sparse enough, people could theoretically back calculate in order to determine who specific people are from the data. In order to prevent people from being able to back calculate, the need to suppress additional data. They were able to accomplish their goal, but at a cost of a runtime of around 15 minutes. For a simple dataset, and one is supposed to be consumable by end users on the fly, any run time of more than a few seconds is unacceptable.

Let’s reproduce a sample using the Sales and Marketing cube. In this example we’ll say that any value below 20,000 and above 0 is sensitive and must be suppressed.


In that screenshot we can see the quantity sold for each product line for each quarter. Here we can see that not a single value is below 20,000. However, what happens if we slice by a specific country?

italy unsuppressed

Here we see the same data set sliced by Italy. We can instantly see many instances of sensitive data being displayed (remember, anything below 20,000 and above 0 is sensitive). For ease of understanding, I’m going to call rename Quantity as “Check Measure”.
Suppressing those values is a simple matter of:

if([Check Measure] not between 1 and 20000) then ([Check Measure]) else (null)

Save that expression in a new data item called Pass 1.

Pass 1

Now we can see sensitive cells are suppressed. Unfortunately it is trivial to recalculate those values where only one cell is suppressed in a column, especially considering that we’re showing a totals row. Each column and row must have either >1 or 0 suppressed cells. To make it even easier to understand, I’ll rename Years to Columns and Product Line to Rows.
First thing to do, create a two new items that finds the next minimum value in the columns. We will want to suppress those values in order to secure the suppressed value. The expression is written to use the memberUniqueName in the rows in order to avoid issues with the next value being a tie.

Columns Min
This finds the minimum value of Check Measure after the values have been filtered out. 
If there are any values suppressed in the first pass, the second pass will filter by values greater than what this returns, effectively suppressing a minimum of 1 more cell than are suppressed.
  , item(
      , 2
      , [Check Measure] 
    , 1

Next, the second pass checks if the current row has one suppressed cell, and if so, it will suppress the cell where the column matches the result from Columns Min

2nd Pass
This will count the number of cells in the columns, and deduct the number of cells remaining. If the value is greater than 0, then a cell is being suppressed - suppress any cells which are equal to the minimum value remaining.

member(if([Pass 1] is null) then ([null]) else (
  total([One] within set [Columns]) - total([Pass 1] within set [Columns])=1) 
then (
  if(roleValue('_memberUniqueName',[Columns]) =[Columns Min]) then ([null]) else ([One])
else ([One])

Pass 2

Now the same basic idea for rows.

Rows Min:

If there are any values suppressed in the first pass, the third pass will filter by values greater than what this returns, effectively suppressing a minimum of 1 more cell than are suppressed.

  , item(
      , 2
      , [Check Measure] 
    , 1

and Pass 3:

3rd Pass
This will count the number of cells in the rows, and deduct the number of cells remaining. If the value is greater than 0, then a cell is being suppressed - suppress any cells which are equal to the minimum value remaining.

if([Pass 2]  is null) then ([null]) else (
  total([One] within set [Rows]) - total([Pass 1] within set [Rows])=1) 

then (
if(roleValue('_memberUniqueName',currentMember(hierarchy([Rows]))) =[Rows Min]) then ([null]) else ([One])

else ([One]))

Again, first it finds all columns that have 1 suppressed cell in the rows then it suppressed the next lowest value:

Pass 3

In this case, there are no more instances of unsecured sensitive cells. However, what happens if we had a data set containing only one sensitive cell? It would suppress another cell in the same row and column, but those two cells would now be at risk. If we slice by the United States the third pass returns the following set:

Pass 3 unsecure

One more pass is needed to hide the remaining intersection:

if([Pass 3] is null) then (null) else (
if ([cc]  = 1) then(
order(filter([Rows], ([cr] >0)and ( [Pass 2]  is not null) )  , [Check Measure] ,basc
) ) then (null) else ( [Final Measure])
else  ( [Final Measure])

cc is a very simple: total([One] within set [Rows]) – total([Pass 2] within set [Rows])
with cr being: total([One] within set [Columns]) – total([Pass 3] within set [Columns])

This is similar to pass 2. It will count the number of suppressed cells in the column. The big change is instead of looking for and suppressing the smallest value the column, we are now looking for a row that already has at least one suppressed cell. It will find the matching rows, order them ascending by number of suppressed cells, and take the last one in the list. Instead of returning a 1 or null, this will return the final measure, in this case revenue.

And the final results, for the United States:
Pass 4

The end result will always be a minimum of 2 rows hidden on each row or column, or none at all. This satisfies the demand for data security on a row level without having to run extremely complex calculations for each possible combination of data in the ETL.

At this point the run time is about 2 seconds (on my laptop), still a long time considering the original query, but far more manageable. It should be mentioned that this is useful for smaller data sets, additional columns and rows will increase the run time significantly. Nested nodes will cause additional complexity, and should probably be avoided.

Sample report can be found here.

Information Security, Risk Management and Back End – Guest Post



Information Security as Risk Management






Framework As Middle Tier






Information Security in Cognos Reports

Cognos has an extremely intricate security system. It is well suited for secure reports and confidential information. Some clients expose portions of their platform to the public, safe in the knowledge that users can only see what they’re allowed to see. However, all of this security can be for nothing through careless report design.

When building any secure reports, it is essential that security be handled in the back end. There have been countless reports built with JavaScript enforced security built on the prompt page, or with security handled with conditional blocks and parameters. Token prompts, while perfect for building dynamic reports, can create holes in which malicious users can attempt to retrieve data that would normally be hidden to them.

This document is not intended to be a guide to hacking Cognos reports. Indeed, many reports would not even require such drastic oversight. Proper usage of macros and filters will solve a majority of the potential problems. Nor does every report need such stringent security. Many times filters are simply used as a means of speeding up the report, or allowing users to narrow their focus.

In this post I will go over possible security flaws with the token prompt.

Consider the following request: A report which shows Employee, Manager, a prompt to choose product line, product type or name. This report will be available to all users to show sales ranking throughout the company.

In order to fulfill the prompt requirement, a token prompt is created with the following static values:

While the data item in the report would be a simple:

When running the report, the user might see the following output:

But a malicious user may attempt to see each employee’s full address.

Nothing in the report was changed, and yet the user can now see confidential information. How was the user able to accomplish this?

Simply by changing the value option in the prompt with IE Developer Tools (each of the major browsers has a similar tool), the user can choose to see any item in the model. “But,” I hear you say, “I can set object level security inside Framework to deny that user group, I’ll even set it on the data layer!” That will certainly work well to prevent users from selecting items from the model, but there is still a work around.

Entering {“EMP_EMPLOYEE_DIM”.”ADDRESS1″ || ‘, ‘ || nvl(“EMP_EMPLOYEE_DIM”.”ADDRESS1″,’ ‘) || ‘, ‘ || “EMP_EMPLOYEE_DIM”.”PROV_STATE” || ‘, ‘ || “EMP_EMPLOYEE_DIM”.”POSTAL_ZONE”} into the value will work just as well, and because it doesn’t reference the data item path, Cognos won’t throw an error. The curly brackets there tell Cognos to execute the code as is. Note that this SQL doesn’t include the country. Cognos won’t attempt to build a join based on custom SQL like this, so including “Branch_region_dimension”.”COUNTRY_EN” would only result in an error.

Since adding custom SQL, like that concatenated string, works, would subqueries also work? In this example any attempt to use a subquery in the data item list would fail, as (at least in Oracle) subqueries are not supported in the Group By. But look what might happen when we remove the two measures from the list:

By adding {(SELECT rtrim (xmlagg (xmlelement (e, owner||’.’||table_name || ‘,’)).extract (‘//text()’), ‘,’) table_names FROM all_tables)} we can now see every single table available. Similar queries can be run to see the fields in each table. At this point the user has full read access to the database, limited entirely by whatever security is applied to the data source user.

The exploits are only possible because the data item contained only the token prompt. I’m not saying that token prompts should never be used; many of my reports are based on the functionality. Instead, use them wisely.

One workaround that I have found is a combination of a new table and a lookup. Build a new table called “TokenExpressions” with the fields “Key”, “Expression”, “Name”, “Report”, and “ReportGroup”. Import that into your framework. Create a parameter map (tokenLookup) based on that table with the TokenExpressions.Report+’-‘+TokenExpressions.Key as Key and TokenExpressions.Expression as Value. In the report, set up your value prompt with the source query as TokenExpressions.Key as Use and TokenExpressions.Name as Display. And finally, in the report queries, use the expression #’ReportName-‘+sq($tokenLookup{prompt(‘Field’,’integer’))}#. In addition to securing the report against unexpected inputs, you also make it significantly easier to add or remove options in the report.

The lesson learned is that using prompts as a security measure is inherently risky. Malicious users with access to web development tools (IE has them by default) may override the existing values and see data that isn’t meant for their eyes.

Finally, it is worth mentioning that using the curly brackets for subqueries can have legitimate uses. I have not found any other way to handle correlated subqueries, for instance. Oracle analytic functions can usually only be accessed through the curly brackets.