Verifiable private multi-party computation: Ranging and ranking

Zhang, Lan; Li, Xiangyang; Jung, Taeho

doi:10.1109/infcom.2013.6566844

Cited by 31 publications

(15 citation statements)

References 13 publications

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“…e.g. [39]- [43]. We need to design a truthful mechanism when the initiator wants to find a target expert within certain hop distance, while tolerating the possibility of multiple experts were found by multiple social referral paths.…”

Section: Discussionmentioning

confidence: 99%

Mechanism design for finding experts using locally constructed social referral web

Zhang

Huang

et al. 2012

2012 Proceedings IEEE INFOCOM

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Abstract-In this work, we address the problem of distributed expert finding using chains of social referrals and profile matching with only local information in online social networks. By assuming that users are selfish, rational, and have privately known cost of participating in the referrals, we design a novel truthful efficient mechanism in which an expert-finding query will be relayed by intermediate users. When receiving a referral request, a participant will locally choose among her neighbors some user to relay the request. In our mechanism, several closely coupled methods are carefully designed to improve the performance of distributed search, including, profile matching, social acquaintance prediction, score function for locally choosing relay neighbors, and budget estimation. We conduct extensive experiments on several datasets of online social networks. The extensive study of our mechanism shows that the success rate of our mechanism is about 90% in finding closely matched experts using only local search and limited budget, which significantly improves the previously best rate 20%. The overall cost of finding an expert by our truthful mechanism is about 20% of the untruthful methods, e.g.. the method that always selects high-degree neighbors. The median length of social referral chains is 6 using our localized search decision, which surprisingly matches the well-known small-world phenomenon of global social structures.

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Section: Discussionmentioning

confidence: 99%

Mechanism design for finding experts using locally constructed social referral web

Zhang

Huang

et al. 2012

2012 Proceedings IEEE INFOCOM

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“…In contrast, our model has the following characteristics: the inputs to the delegated functions include other data owners' private data sets, which are not known to the delegators in advance. Finally, [38] considered the notion of verifiable private multi-party computation, but not in the setting of outsourcing data and functions to the cloud.…”

Section: Related Workmentioning

confidence: 99%

Verifiable Delegated Set Intersection Operations on Outsourced Encrypted Data

Zheng

2015

2015 IEEE International Conference on Cloud Engineering

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Abstract-We initiate the study of the following problem: Suppose Alice and Bob would like to outsource their encrypted private data sets to the cloud, and they also want to conduct the set intersection operation on their plaintext data sets. The straightforward solution for them is to download their outsourced ciphertexts, decrypt the ciphertexts locally, and then execute a commodity twoparty set intersection protocol. Unfortunately, this solution is not practical. We therefore motivate and introduce the novel notion of Verifiable Delegated Set Intersection on outsourced encrypted data (VDSI). The basic idea is to delegate the set intersection operation to the cloud, while (i) not giving the decryption capability to the cloud, and (ii) being able to hold the misbehaving cloud accountable. We formalize security properties of VDSI and present a construction. In our solution, the computational and communication costs on the users are linear to the size of the intersection set, meaning that the efficiency is optimal up to a constant factor.

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“…That is, the DDH problem is easier than the CDH one, and if a problem is as hard as a DDH problem, it is harder than a CDH problem. Our protocol is based on the assumption that it is computational expensive to solve the CDH problem as in other similar research works ( [22], [23], [24], [25], [26], [27]). Then, we define the security of our privacy-preserving sum and product calculation as follows.…”

Section: Security Modelmentioning

confidence: 99%

Collusion-Tolerable Privacy-Preserving Sum and Product Calculation without Secure Channel

Jung

Wan

2015

IEEE Trans. Dependable and Secure Comput.

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Abstract-Much research has been conducted to securely outsource multiple parties' data aggregation to an untrusted aggregator without disclosing each individual's privately owned data, or to enable multiple parties to jointly aggregate their data while preserving privacy. However, those works either require secure pair-wise communication channels or suffer from high complexity. In this paper, we consider how an external aggregator or multiple parties can learn some algebraic statistics (e.g., sum, product) over participants' privately owned data while preserving the data privacy. We assume all channels are subject to eavesdropping attacks, and all the communications throughout the aggregation are open to others. We first propose several protocols that successfully guarantee data privacy under semi-honest model, and then present advanced protocols which tolerate up to k passive adversaries who do not try to tamper the computation. Under this weak assumption, we limit both the communication and computation complexity of each participant to a small constant. At the end, we present applications which solve several interesting problems via our protocols.

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Verifiable private multi-party computation: Ranging and ranking

Cited by 31 publications

References 13 publications

Mechanism design for finding experts using locally constructed social referral web

Mechanism design for finding experts using locally constructed social referral web

Verifiable Delegated Set Intersection Operations on Outsourced Encrypted Data

Collusion-Tolerable Privacy-Preserving Sum and Product Calculation without Secure Channel

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