Collusion-resistant Scheme Research Articles

A blockchain-based privacy-preserving and collusion-resistant scheme (PPCR) for double auctions

Electronic auctions (e-auctions) remove the physical limitations of traditional auctions and bring this mechanism to the general public. However, most e-auction schemes involve a trusted auctioneer, which is not always credible in practice. Some studies have applied cryptography tools to solve this problem by distributing trust, but they ignore the existence of collusion. In this paper, a blockchain-based Privacy-Preserving and Collusion-Resistant scheme (PPCR) for double auctions is proposed by employing both cryptography and blockchain technology, which is the first decentralized and collusion-resistant double auction scheme that guarantees bidder anonymity and bid privacy. A two-server-based auction framework is designed to support off-chain allocation with privacy preservation and on-chain dispute resolution for collusion resistance. A Dispute Resolution agreement (DR) is provided to the auctioneer to prove that they have conducted the auction correctly and the result is fair and correct. In addition, a Concise Dispute Resolution protocol (CDR) is designed to handle situations where the number of accused winners is small, significantly reducing the computation cost of dispute resolution. Extensive experimental results confirm that PPCR can indeed achieve efficient collusion resistance and verifiability of auction results with low on-chain and off-chain computational overhead.

Collusion-Resistant Worker Recruitment in Crowdsourcing Systems

In the wake of the Web 2.0, crowdsourcing has emerged as a promising approach to maintain a flexible workforce for human intelligence tasks. To stimulate worker participation, many reverse auction-based incentive mechanisms have been proposed. Designing auctions that discourage workers from cheating and instead encouraging them to reveal their true cost information has drawn significant attention. However, the existing efforts have been focusing on tackling individual cheating misbehaviors, while the scenarios that workers strategically form collusion coalitions and rig their bids together to manipulate auction outcomes have received little attention. To fill this gap, in this work we develop a <inline-formula><tex-math notation="LaTeX">$(t,p)$</tex-math></inline-formula> -collusion resistant scheme that ensures no coalition of <i>weighted cardinality</i> <inline-formula><tex-math notation="LaTeX">$t$</tex-math></inline-formula> can improve its group utility by coordinating the bids at a probability of <inline-formula><tex-math notation="LaTeX">$p$</tex-math></inline-formula> . This paper takes into account the unique features of crowdsourcing, such as diverse worker types and reputations, in the design. The proposed scheme can suppress a broad spectrum of collusion strategies. Besides, desirable properties, including <inline-formula><tex-math notation="LaTeX">$p$</tex-math></inline-formula> -truthfulness and <inline-formula><tex-math notation="LaTeX">$p$</tex-math></inline-formula> -individual rationality, are also achieved. To provide a comprehensive evaluation, we first analytically prove our scheme's collusion resistance and then experimentally verify our analytical conclusion using a real-world dataset. Our experimental results show that the baseline scheme, where none of the critical properties is guaranteed, costs up to 20.1 times the optimal payment in an ideal case where no collusion exists, while our final scheme is merely 4.9 times the optimal payment.

Privacy-preserving frequent itemset mining in vertically partitioned database using symmetric homomorphic encryption scheme

Frequent itemset mining (FIM) and association rule mining (ARM) are two popular and widely used mining techniques for transactional databases with wide range of applications such as medical diagnosis, market basket analysis, protein sequences, census data, etc. In privacy-preserving frequent itemset mining (PPFIM) over vertically partitioned database, data owners wish to learn frequent itemsets from their concatenated dataset without disclosing their sensitive information. A collusion resistant scheme has been proposed using Du-Atallah's SBDP protocol for multiparty multi-vector scenario which works under two party two-vector case. Also, a scheme is designed by generalising the Du-Atallah's scheme for more than two parties. However it fails in privacy requirements when more than two parties/vectors are considered. In this paper, we give a critique on this approach and focus on efficient and more secure methods for privacy preserving FIM in vertically partitioned databases. To ensure privacy of raw data of different data owners having heterogeneous attributes, we proposed a secure-sum algorithm which uses symmetric homomorphic encryption scheme as a sub-part and a semihonest trusted third party (STTP) environment for calculating the support count of itemsets in privacy preserving manner.

Privacy-preserving frequent itemset mining in vertically partitioned database using symmetric homomorphic encryption scheme

Frequent itemset mining (FIM) and association rule mining (ARM) are two popular and widely used mining techniques for transactional databases with wide range of applications such as medical diagnosis, market basket analysis, protein sequences, census data, etc. In privacy-preserving frequent itemset mining (PPFIM) over vertically partitioned database, data owners wish to learn frequent itemsets from their concatenated dataset without disclosing their sensitive information. A collusion resistant scheme has been proposed using Du-Atallah's SBDP protocol for multiparty multi-vector scenario which works under two party two-vector case. Also, a scheme is designed by generalising the Du-Atallah's scheme for more than two parties. However it fails in privacy requirements when more than two parties/vectors are considered. In this paper, we give a critique on this approach and focus on efficient and more secure methods for privacy preserving FIM in vertically partitioned databases. To ensure privacy of raw data of different data owners having heterogeneous attributes, we proposed a secure-sum algorithm which uses symmetric homomorphic encryption scheme as a sub-part and a semihonest trusted third party (STTP) environment for calculating the support count of itemsets in privacy preserving manner.

Fully collusion-resistant traitor tracing scheme with shorter ciphertexts

A traitor tracing scheme allows a content distributor to detect at least one of the traitors whose secret key is used to create a pirate decoder. In building efficient traitor tracing schemes, reducing ciphertext size is a significant factor since the traitor tracing scheme must handle a larger number of users. In this paper, we present a fully collusion-resistant traitor tracing scheme where the ciphertext size is 2.8 times shorter and encryption time is 2.6 times faster, compared to the best cases of fully collusion-resistant schemes previously suggested. We can achieve these efficiency results without sacrificing other costs. Also, our scheme supports public tracing and black-box tracing. To achieve our goal, we use asymmetric bilinear maps in prime order groups, and we introduce a new cancellation technique that has the same effect as that in composite order groups.