First steps with Tumult Analytics#

In this first tutorial, we will demonstrate how to load data, run a simple aggregation query, and get our first differentially private results. You can run this tutorial (as well as the next ones) as you go: simply follow the installation instructions, and use the copy/paste button of each code block to reproduce it.

Throughout these tutorials, we’ll imagine we are the data protection officer for a fictional institution, the Pierre-Simon Laplace Public Library. We want to publish statistics about how our library serves the needs of its community. Of course, we have the privacy of our members at heart, so we want to make sure that the data we release does not reveal anything about specific people.

This is a perfect use case for differential privacy: it will allow us to publish useful insights about groups, while protecting data about individuals. Importantly, Tumult Analytics does not require you to have an in-depth understanding of differential privacy. In these tutorials, we will gloss over all the details of what happens behind the scenes, and focus on how to accomplish common tasks. To learn more about the trade-offs involved in parameter setting and mechanism design, you can consult our topic guides.

Setup#

First, let’s import some Python packages.

from pyspark import SparkFiles
from pyspark.sql import SparkSession
from tmlt.analytics import (
    AddOneRow,
    PureDPBudget,
    QueryBuilder,
    Session,
)

Next, we initialize the Spark session.

spark = SparkSession.builder.getOrCreate()

This creates an Analytics-ready Spark Session. For more details on using Spark sessions with Analytics, or to troubleshoot, see the Spark topic guide.

Now, we need to load our first dataset, containing information about the members of our public library. Here, we get the data from a public s3 repository, and load it into a Spark DataFrame.

spark.sparkContext.addFile(
    "https://tumult-public.s3.amazonaws.com/library-members.csv"
)
members_df = spark.read.csv(
    SparkFiles.get("library-members.csv"), header=True, inferSchema=True
)

For more information about loading data files into Spark, see the Spark data sources documentation.

Creating a Session#

To compute queries using Tumult Analytics, we must first wrap the data in a Session to track and manage queries. The following snippet instantiates a Session with a DataFrame of our private data using the from_dataframe() method.

session = Session.from_dataframe(
    privacy_budget=PureDPBudget(3),
    source_id="members",
    dataframe=members_df,
    protected_change=AddOneRow(),
)

Note that in addition to the data itself, we needed to provide a couple of additional pieces of information:

  • The privacy_budget specifies what privacy guarantee this Session will provide. We will discuss this in more detail in the next tutorial.

  • The source_id is the identifier for the DataFrame. We will then use it to refer to this DataFrame when constructing queries.

  • The protected_change for this dataset, which defines what unit of data the differential privacy guarantee holds for. Here, AddOneRow() corresponds to protecting individual rows in the dataset.

For a more complete description of the various ways a Session can be initialized, you can consult the relevant topic guide. For more complex values for the protected_change parameter, see the privacy promise topic guide and the protected_change API documentation.

Evaluating queries in a Session#

Now that we have our Session, we can ask our first query. How many members does our library have? To answer this question with a query, we will use the QueryBuilder interface.

count_query = QueryBuilder("members").count()

The first part, QueryBuilder("members"), specifies which private data we want to run the query on; this corresponds to the source_id parameter from earlier. Then, the count() statement requests the total number of rows in the dataset.

After creating our query, we need to actually run it on the data, using the evaluate method of our Session. This requires us to allocate some privacy budget to this evaluation: here, let’s evaluate the query with differential privacy, using ε=1.

total_count = session.evaluate(
    count_query,
    privacy_budget=PureDPBudget(epsilon=1)
)

The results of the query are returned as a Spark DataFrame. We can see them using the show() method of this DataFrame.

total_count.show()

+-----+
|count|
+-----+
|54215|
+-----+

We have just evaluated our first differentially private query! If you’re running this code along with the tutorial, you might see different values. This is a central characteristic of differential privacy: it injects some randomization (we call this noise) in the execution of the query. Let’s evaluate the same query again to demonstrate this.

total_count = session.evaluate(
    count_query,
    privacy_budget=PureDPBudget(1)
)
total_count.show()

+-----+
|count|
+-----+
|54218|
+-----+

The query result is slightly different from the previous one.

The noise added to the computation of the query can depend on the privacy parameters, the type of aggregation, and the data itself. But in many cases, the result will still convey accurate insights about the original data. Here, that’s the case: we can verify this by running a count query directly on the original DataFrame, which gives us the true result.

total_count = members_df.count()
print(total_count)

54217

We have evaluated a differentially private count, and seen how the result relates to the true value for this count. In the next tutorial, we’ll say a bit more about how privacy budgets work in practice, and evaluate some more complicated queries.