The application of differential privacy for rank aggregation: Privacy and accuracy
- Resource Type
- Conference
- Authors
- Shang, Shang; Wang, Tiance; Cuff, Paul; Kulkarni, Sanjeev
- Source
- 17th International Conference on Information Fusion (FUSION) Information Fusion (FUSION), 2014 17th International Conference on. :1-7 Jul, 2014
- Subject
- Aerospace
Computing and Processing
General Topics for Engineers
Photonics and Electrooptics
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Privacy
Error analysis
Noise
Upper bound
Histograms
Algorithm design and analysis
Vectors
Rank Aggregation
Accuracy
- Language
The potential risk of privacy leakage prevents users from sharing their honest opinions on social platforms. This paper addresses the problem of privacy preservation if the query returns the histogram of rankings. The framework of differential privacy is applied to rank aggregation. The error probability of the aggregated ranking is analyzed as a result of noise added in order to achieve differential privacy. Upper bounds on the error rates for any positional ranking rule are derived under the assumption that profiles are uniformly distributed. Simulation results are provided to validate the probabilistic analysis.