Table 3 Dataset description
From: Enhancing correlated big data privacy using differential privacy and machine learning
S. no. | Dataset | Attributes | Attributes considered for experimental purpose |
|---|---|---|---|
1 | New-York-City trip data-green Taxi-2016 | VendorID, lpep pickup datetime, Lpep dropoff datetime, Store and fwd flag, RateCodeID, Pickup longitude, Pickup latitude, Dropoff Longitude, Dropoff latitude, Passenger count, Trip distance, Fare Amount, Extra, MTA tax, Tip amount, Tolls amount, Ehail fee, improvement surcharge, Total amount, Payment type, Trip type | VendorID, RateCode, Pickup longitude, Pickup latitude, Dropoff Longitude, Dropoff Latitude, Passenger count, Trip distance, Fare Amount, Extra, MTA tax, Tip amount, Tolls amount, improvement surcharge, Total amount, Payment type, Trip type |
2 | Chicago Crime Data | Id, caseNo, date, block, IUCR, Primarytype, Description, location, arrest, domestic, beat, district, ward, community area, FBIcode, xcoordinate, ycoordinate, year, updated, latitude, longitude, location | Id, caseNo, date, block, IUCR, arrest, domestic, beat, district, community area, FBIcode, xcoordinate, ycoordinate, year, updated, latitude, longitude, location |
3 | New-York-City trip data-2013 | Store and fwd flag, rate code, Dropoff latitude, Passenger count, Trip distance, Fare amount, Extra, MTA tax, Trip amount, Tolls amount, Ehail fee, Total payment, Trip type, Pickup Longitude, Pickup Latitude, Droffoff longitude, Vendor id, pickup hour, pickup day, Pickup month, pickup year, pickup minute, Dropoff hour, Dropoff day, Dropoff month, Dropoff year, Dropoff minute | Store and fwd flag ,rate code, Dropoff latitude, Passenger count, Trip distance, Fare amount, Extra, MTA tax, Trip amount, Tolls amount, Total payment, Pickup Longitude, Pickup Latitude, Droffoff longitude, Vendor id, pickup hour, pickup day, Pickup month, pickup year, pickup minute, Dropoff hour, Dropoff day, Dropoff month, Dropoff year, Dropoff minute |