Auction is an effective way to allocate goods or services to bidders who value them the most. The rapid growth of e-auctions facilitates online transactions but poses new and distinctive challenges. It is difficult to establish trusts among sellers, buyers and auctioneers without the centralized auction websites or platforms (the auctioneer) that collect bids and derive the auction results. However, these third parties may be untrustworthy, and malicious sellers or buyers may refuse to deliver the goods or payment according to the protocol. Moreover, the open and anonymous online environment may stimulate auction participants to form collusion coalitions to rig the auction and reap unfair profit. Many auction designs have been proposed to address these concerns, but they fall short of simultaneously achieving decentralization (i.e., held without a trusted third utility), strong consensus (i.e., the establishment of trust), collusion-resistance and practical implementation. We present CReam, the first decentralized collusion-resistant e-auction system that is implemented with smart contract on the blockchain. With the carefully-designed smart auction contract, mutually distrustful and rational sellers and buyers are stimulated to operate properly hence transact safely without trusted third parties. The auction mechanism in the smart contract can effectively prevent bidder collusion and realize economic robustness, i.e., truthfulness. We implement a fully functional CReam on the Ethereum network. Extensive experimental results confirm that CReam can greatly reduce the probability of collusion and achieve an approximate optimal revenue at a low cost of contract execution.

Multimodal contrastive learning aims to train a general-purpose feature extractor, such as CLIP, on vast amounts of raw, unlabeled paired image-text data. This can greatly benefit various complex downstream tasks, including cross-modal image-text retrieval and image classification. Despite its promising prospect, the security issue of cross-modal pre-trained encoder has not been fully explored yet, especially when the pre-trained encoder is publicly available for commercial use. In this work, we propose AdvCLIP, the first attack framework for generating downstream-agnostic adversarial examples based on cross-modal pre-trained encoders. AdvCLIP aims to construct a universal adversarial patch for a set of natural images that can fool all the downstream tasks inheriting the victim cross-modal pre-trained encoder. To address the challenges of heterogeneity between different modalities and unknown downstream tasks, we first build a topological graph structure to capture the relevant positions between target samples and their neighbors. Then, we design a topology-deviation based generative adversarial network to generate a universal adversarial patch. By adding the patch to images, we minimize their embeddings similarity to different modality and perturb the sample distribution in the feature space, achieving unviersal non-targeted attacks. Our results demonstrate the excellent attack performance of AdvCLIP on two types of downstream tasks across eight datasets. We also tailor three popular defenses to mitigate AdvCLIP, highlighting the need for new defense mechanisms to defend cross-modal pre-trained encoders. Our codes are available at: https://github.com/CGCL-codes/AdvCLIP.

Prediction-as-a-service based on neural networks becomes popular especially in the outsourced setting. Recent researches focus on the privacy of the query and results, but they do not provide model privacy and may leak partial information about the results. Some of them further require frequent interactions with the querier or heavy computation overheads. This paper proposes a new scheme for privacy-preserving neural network prediction in the outsourced setting, i.e., the server cannot learn the query, (intermediate) results, and the model. Similar to SecureML (S&P'17), a representative work which provides model privacy, we leverage two non-colluding servers with secret sharing and triplet generation to minimize the usage of heavyweight cryptography. Further, we adopt asynchronous computation to improve the throughput, and design a garbled circuit for the non-polynomial activation function to keep the same accuracy as the underlying network (instead of approximating it). Our experiments on four neural network architectures show that our scheme achieves an average of 282x improvements in reducing latency compared to SecureML. Compared to MiniONN (CCS'17) and EzPC (EuroS&P'19), both without model privacy, our scheme has 18x and 10x lower latency, respectively. For the communication costs, our scheme outperforms SecureML by 122x, MiniONN by 49x, and EzPC by 38x.

Biometric identification typically scans a large-scale database of biometric records for finding a close enough match of an individual. This paper investigates how to outsource this computationally expensive scanning while protecting the privacy of both the database and the computation. Exploiting the inherent structures of biometric data and the properties of identification operations, we first present a privacy-preserving biometric identification scheme which uses a single server. We then consider its extensions in the two-server model. It achieves a higher level of privacy than our single-server solution assuming two servers are not colluding. Apart from somewhat homomorphic encryption, our second scheme uses batched protocols for secure shuffling and minimum selection. Our experiments on both synthetic and real data sets show that our solutions outperform existing schemes while preserving privacy.

Image retrieval is crucial for social media sites such as Instagram to identify similar images and make recommendations for users who share similar interests. To get rid of the storage burden and computation for image retrieval, outsourcing to a remote cloud is now a trend. Yet, privacy concerns mandate the use of encryption before outsourcing the images. We need a secure way for retrieving images from a not-fully-trusted server. This paper proposes InstantCryptoGram, a secure image retrieval service. We first design a new data structure called sub-simhash, which fits for the inverted index used by many searchable symmetric encryption schemes. It leads to our modular solution that supports efficient similarity queries and updates over encrypted images. Our experiments on Amazon AWS EC2 over representative datasets show that our scheme is efficient and accurate in finding similar images while preserving privacy.

Dynamic spectrum allocation via auction is an effective solution to spectrum shortage. Combinatorial spectrum auction enables buyers to express diversified preferences towards different combinations of channels. Despite the effort to ensure truthfulness and maximize social welfare, spectrum auction also faces potential security risks. The leakage of sensitive information such as true valuation and location of bidders may incur severe economic damage. However, there is a lack of works that can provide sufficient protection against such security risks in combinatorial spectrum auction. In this paper, we propose ARMOR, to enable combinatorial auction for heterogeneous spectrum with privacy, which can preserve bidders' privacy while guaranteeing the economic-robustness of the combinatorial auction. We leverage the cryptographic methods, including homomorphic encryption, statistical masking and garbled circuits, to shield the bid and location information of buyers from the auctioneer. We design a novel location protection algorithm, which allows the auctioneer to exploit spectrum reuse opportunities without knowing the exact locations of buyers. Furthermore, we propose a verifiable payment scheme based on digital signature to prevent the auctioneer from forging the payment. The extensive experiments confirm that ARMOR maintains the good performance of the combinatorial spectrum auction, in terms of buyer satisfactory ratio and social welfare, and achieves privacy preservation with acceptable computation and communication costs.