NDLI: Enabling privacy-preserving auctions in big data

Content Provider	IEEE Xplore Digital Library
Author	Taeho Jung Xiang-Yang Li
Copyright Year	2015
Description	Author affiliation: Dept. of Comput. Sci., Illinois Inst. of Technol., Chicago, IL, USA (Taeho Jung; Xiang-Yang Li)
Abstract	We study how to enable auctions in the big data context to solve many upcoming data-based decision problems in the near future. We consider the characteristics of the big data including, but not limited to, velocity, volume, variety, and veracity, and we believe any auction mechanism design in the future should take the following factors into consideration: 1) generality (variety); 2) efficiency and scalability (velocity and volume); 3) truthfulness and verifiability (veracity). In this paper, we propose a privacy-preserving construction for auction mechanism design in the big data, which prevents adversaries from learning unnecessary information except those implied in the valid output of the auction. More specifically, we considered one of the most general form of the auction (to deal with the variety), and greatly improved the the efficiency and scalability by approximating the NP-hard problems and avoiding the design based on garbled circuits (to deal with velocity and volume), and finally prevented stakeholders from lying to each other for their own benefit (to deal with the veracity). The comparison with peer work shows that we greatly improved the asymptotic performance of peer works' overhead from the exponential growth to a linear growth and from linear growth to a logarithmic growth, which greatly contributes to the scalability of our mechanism.
Starting Page	173
Ending Page	178
File Size	319394
Page Count	6
File Format	PDF
ISBN	9781467371315
DOI	10.1109/INFCOMW.2015.7179380
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-04-26
Publisher Place	Hong Kong
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data privacy Bismuth Big data Silicon Approximation methods Cryptography Resource management
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Enabling Privacy-preserving Auctions in Big Data

Enabling privacy-preserving auctions in big data (2015)

Enabling Multi-level trust in privacy Preserving Data mining

Privacy Preserving Data Analytics for Smart Homes

Privacy-Preserving Layer over MapReduce on Cloud

A privacy preserving scheme for change increment of electricity big data base on symmetric cryptography

Privacy Preserving Data Communication Model

MACA: A privacy-preserving multi-factor cloud authentication system utilizing big data

Multi-cloud privacy preserving schemes for linear data mining

Enabling privacy-preserving auctions in big data

Similar Documents

Enabling Privacy-preserving Auctions in Big Data

Enabling privacy-preserving auctions in big data (2015)

Enabling Multi-level trust in privacy Preserving Data mining

Privacy Preserving Data Analytics for Smart Homes

Privacy-Preserving Layer over MapReduce on Cloud

A privacy preserving scheme for change increment of electricity big data base on symmetric cryptography

Privacy Preserving Data Communication Model

MACA: A privacy-preserving multi-factor cloud authentication system utilizing big data

Multi-cloud privacy preserving schemes for linear data mining

Enabling privacy-preserving auctions in big data