Step 1: Data retrieval from BidStream
I will grant us viewer access in BidStream, which will allow us to extract all bidding data passing through a specific geographical area. The data will be saved for two days so we need to develop a mechanism that will collect it.

Step 2: Supplementary dataset
I will provide a complete list of phone numbers from the same geographical area, including associated addresses.

Step 3: User behavior analysis
We will collect bids associated with unique users based on defined digital behavior patterns. For example, if a user views the same ad multiple times within a particular location, that site may correspond to their home or office. Time-of-day patterns will help determine whether the location is more likely a residence or workplace.

Step 4: Rainbow table generation
We will create a rainbow table linking each unique advertising identifier (AdID) to its inferred home address and phone operator.

Step 5: User dataset alignment
We will repeat Step 4 until we have a balanced dataset containing an equal number of unique users (from BidStream data) and phone numbers (from the provided list).

Step 6: Data matching and correlation
We will match AdIDs to phone numbers using a probability-based mechanism. The cross-referencing will primarily rely on shared data points such as address and phone provider. Adding demographic details like gender or age, where available, will further improve the matching accuracy.

Note: iceberg catalog (like CloudFlare R2 data catalog) is the cheapest solution for storing bidstream, like good solution to store and process lots of data using S3 like storage.

Note: regarding lat and long data in bidstream: unfortunately it's an ip to geo. It means that this data is not matching real user location, it's just geoip.