Academic Journal

Incentive Compatible PrivacyPreserving Data Analysis

التفاصيل البيبلوغرافية
العنوان: Incentive Compatible PrivacyPreserving Data Analysis
المؤلفون: Murat Kantarcioglu, Wei Jiang
المساهمون: The Pennsylvania State University CiteSeerX Archives
المصدر: http://www.utdallas.edu/%7Emxk055100/publications/incentive-ppda.pdf.
سنة النشر: 2013
المجموعة: CiteSeerX
الوصف: —In many cases, competing parties who have private data may collaboratively conduct privacy-preserving distributed data analysis (PPDA) tasks to learn beneficial data models or analysis results. Most often, the competing parties have different incentives. Although certain PPDA techniques guarantee that nothing other than the final analysis result is revealed, it is impossible to verify whether participating parties are truthful about their private input data. Unless proper incentives are set, current PPDA techniques cannot prevent participating parties from modifying their private inputs. This raises the question of how to design incentive compatible privacy-preserving data analysis techniques that motivate participating parties to provide truthful inputs. In this paper, we first develop key theorems, then base on these theorems, we analyze certain important privacy-preserving data analysis tasks that could be conducted in a way that telling the truth is the best choice for any participating party. Index Terms—Privacy, secure multiparty computation, noncooperative computation Ç 1
نوع الوثيقة: text
وصف الملف: application/pdf
اللغة: English
Relation: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.676.1120; http://www.utdallas.edu/%7Emxk055100/publications/incentive-ppda.pdf
الاتاحة: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.676.1120
http://www.utdallas.edu/%7Emxk055100/publications/incentive-ppda.pdf
Rights: Metadata may be used without restrictions as long as the oai identifier remains attached to it.
رقم الانضمام: edsbas.F19D5322
قاعدة البيانات: BASE