RPM: Local Differential Privacy based Inventory Forecasting
Lan Zhang, Cong Tang · 2021
In the Cost-Per-Mille (CPM) advertising business scenario that adopts the guaranteed delivery model, ad publisher predicts the future available impressions inventory of each advertising placement under the constraints of each target audience based on the historical impressions data, so as to sell advertising resources reasonably. However, historical data threatens user and publisher privacy during the Sharing-Aggregation-Analysis procedure. In addition, internet companies that act as ad publishers are reluctant to release data constrained by laws and regulations, which creates Isolated Data Island. In order to realize privacy-preserving data sharing and publishing and improve data availability as much as possible to build inventory structure, we propose a novel Local Differential Privacy (LDP) method RPM to process user impressions logs and design a privacy-preserving inventory forecasting(PPIF) framework combining clustering and sampling in this work. LDP is a promising privacy standard. There is no need for trusted third parties to participate. Each user perturbs sensitive data locally and uploads the disturbed records. The real data does not leave the local area, providing the user with the privacy preserving effect of indistinguishable individuals. There is a trade-off between privacy protection and data availability. Under availability constraints, LDP is usually used in industry only for frequency and mean statistics operations. There is plenty of room for research in designing foundational LDP technologies to support more data-mining computing tasks such as clustering. The privacy-preserving effect of RPM is demonstrated by rigorous mathematical proof, and the simulation experiments prove that the inventory estimation results obtained by using RPM are more accurate than other existing LDP methods.