Package: smartnoise-sql
SmartNoise SQLDifferentially private SQL queries.  Tested with:PostgreSQLSQL ServerSparkPandas (SQLite)PrestoDBBigQuerySmartNoise is intended for scenarios where the analyst is trusted by the data owner.  SmartNoise uses theOpenDPlibrary of differential privacy algorithms.Installationpip install smartnoise-sqlQuerying a Pandas DataFrameUse thefrom_dfmethod to create a private reader that can issue queries against a pandas dataframe. Example below uses datasetsPUMS.csvandPUMS.yamlcan be found in thedatasetsfolder in the root directory.importsnsqlfromsnsqlimportPrivacyimportpandasaspdprivacy=Privacy(epsilon=1.0,delta=0.01)csv_path='PUMS.csv'meta_path='PUMS.yaml'pums=pd.read_csv(csv_path)reader=snsql.from_df(pums,privacy=privacy,metadata=meta_path)result=reader.execute('SELECT sex, AVG(age) AS age FROM PUMS.PUMS GROUP BY sex')Querying a SQL DatabaseUsefrom_connectionto wrap an existing database connection.The connection must be to a database that supports the SQL standard,
in this example the database must be configured with the namePUMS, have a schema calledPUMSand a table calledPUMS, and the data fromPUMS.csvneeds to be in that table.importsnsqlfromsnsqlimportPrivacyimportpsycopg2privacy=Privacy(epsilon=1.0,delta=0.01)meta_path='PUMS.yaml'pumsdb=psycopg2.connect(user='postgres',host='localhost',database='PUMS')reader=snsql.from_connection(pumsdb,privacy=privacy,metadata=meta_path)result=reader.execute('SELECT sex, AVG(age) AS age FROM PUMS.PUMS GROUP BY sex')Querying a Spark DataFrameUsefrom_connectionto wrap a spark session.importpysparkfrompyspark.sqlimportSparkSessionspark=SparkSession.builder.getOrCreate()fromsnsqlimport*pums=spark.read.load(...)# load a Spark DataFramepums.createOrReplaceTempView("PUMS_large")metadata='PUMS_large.yaml'private_reader=from_connection(spark,metadata=metadata,privacy=Privacy(epsilon=3.0,delta=1/1_000_000))private_reader.reader.compare.search_path=["PUMS"]res=private_reader.execute('SELECT COUNT(*) FROM PUMS_large')res.show()Privacy CostThe privacy parameters epsilon and delta are passed in to the private connection at instantiation time, and apply to each computed column during the life of the session.  Privacy cost accrues indefinitely as new queries are executed, with the total accumulated privacy cost being available via thespentproperty of the connection'sodometer:privacy=Privacy(epsilon=0.1,delta=10e-7)reader=from_connection(conn,metadata=metadata,privacy=privacy)print(reader.odometer.spent)# (0.0, 0.0)result=reader.execute('SELECT COUNT(*) FROM PUMS.PUMS')print(reader.odometer.spent)# approximately (0.1, 10e-7)The privacy cost increases with the number of columns:reader=from_connection(conn,metadata=metadata,privacy=privacy)print(reader.odometer.spent)# (0.0, 0.0)result=reader.execute('SELECT AVG(age), AVG(income) FROM PUMS.PUMS')print(reader.odometer.spent)# approximately (0.4, 10e-6)The odometer is advanced immediately before the differentially private query result is returned to the caller.  If the caller wishes to estimate the privacy cost of a query without running it,get_privacy_costcan be used:reader=from_connection(conn,metadata=metadata,privacy=privacy)print(reader.odometer.spent)# (0.0, 0.0)cost=reader.get_privacy_cost('SELECT AVG(age), AVG(income) FROM PUMS.PUMS')print(cost)# approximately (0.4, 10e-6)print(reader.odometer.spent)# (0.0, 0.0)Note that the total privacy cost of a session accrues at a slower rate than the sum of the individual query costs obtained byget_privacy_cost.  The odometer accrues all invocations of mechanisms for the life of a session, and uses them to compute total spend.reader=from_connection(conn,metadata=metadata,privacy=privacy)query='SELECT COUNT(*) FROM PUMS.PUMS'epsilon_single,_=reader.get_privacy_cost(query)print(epsilon_single)# 0.1# no queries executed yetprint(reader.odometer.spent)# (0.0, 0.0)for_inrange(100):reader.execute(query)epsilon_many,_=reader.odometer.spentprint(f'{epsilon_many}<{epsilon_single*100}')HistogramsSQLgroup byqueries represent histograms binned by grouping key.  Queries over a grouping key with unbounded or non-public dimensions expose privacy risk. For example:SELECTlast_name,COUNT(*)FROMSalesGROUPBYlast_nameIn the above query, if someone with a distinctive last name is included in the database, that person's record might accidentally be revealed, even if the noisy count returns 0 or negative.  To prevent this from happening, the system will automatically censor dimensions which would violate differential privacy.Private SynopsisA private synopsis is a pre-computed set of differentially private aggregates that can be filtered and aggregated in various ways to produce new reports.  Because the private synopsis is differentially private, reports generated from the synopsis do not need to have additional privacy applied, and the synopsis can be distributed without risk of additional privacy loss.  Reports over the synopsis can be generated with non-private SQL, within an Excel Pivot Table, or through other common reporting tools.You can see a samplenotebook for creating private synopsissuitable for consumption in Excel or SQL.LimitationsYou can think of the data access layer as simple middleware that allows composition ofopendpcomputations using the SQL language.  The SQL language provides a limited subset of what can be expressed through the fullopendplibrary.  For example, the SQL language does not provide a way to set per-field privacy budget.Because we delegate the computation of exact aggregates to the underlying database engines, execution through the SQL layer can be considerably faster, particularly with database engines optimized for precomputed aggregates.  However, this design choice means that analysis graphs composed with SQL language do not access data in the engine on a per-row basis.  Therefore, SQL queries do not currently support algorithms that require per-row access, such as quantile algorithms that use underlying values.  This is a limitation that future releases will relax for database engines that support row-based access, such as Spark.The SQL processing layer has limited support for bounding contributions when individuals can appear more than once in the data.  This includes ability to perform reservoir sampling to bound contributions of an individual, and to scale the sensitivity parameter.  These parameters are important when querying reporting tables that might be produced from subqueries and joins, but require caution to use safely.For this release, we recommend using the SQL functionality while bounding user contribution to 1 row.  The platform defaults to this option by settingmax_contribto 1, and should only be overridden if you know what you are doing.  Future releases will focus on making these options easier for non-experts to use safely.CommunicationYou are encouraged to join us onGitHub DiscussionsPlease useGitHub Issuesfor bug reports and feature requests.For other requests, including security issues, please contact us atsmartnoise@opendp.org.Releases and ContributingPlease let us know if you encounter a bug bycreating an issue.We appreciate all contributions. Please review thecontributors guide. We welcome pull requests with bug-fixes without prior discussion.If you plan to contribute new features, utility functions or extensions, please first open an issue and discuss the feature with us.