Incomplete scenario is a prevalent, practical, yet challenging setting in Multimodal Recommendations (MMRec), where some item modalities are missing due to various factors. Recently, a few efforts have sought to improve the recommendation accuracy by exploring generic structures from incomplete data. However, two significant gaps persist: 1) the difficulty in accurately generating missing data due to the limited ability to capture modality distributions; and 2) the critical but overlooked visibility bias, where items with missing modalities are more likely to be disregarded due to the prioritization of items' multimodal data over user preference alignment. This bias raises serious concerns about the fair treatment of items. To bridge these two gaps, we propose a novel Modality-Diffused Counterfactual (MoDiCF) framework for incomplete multimodal recommendations. MoDiCF features two key modules: a novel modality-diffused data completion module and a new counterfactual multimodal recommendation module. The former, equipped with a particularly designed multimodal generative framework, accurately generates and iteratively refines missing data from learned modality-specific distribution spaces. The latter, grounded in the causal perspective, effectively mitigates the negative causal effects of visibility bias and thus assures fairness in recommendations. Both modules work collaboratively to address the two aforementioned significant gaps for generating more accurate and fair results. Extensive experiments on three real-world datasets demonstrate the superior performance of MoDiCF in terms of both recommendation accuracy and fairness. The code and processed datasets are released at https://github.com/JinLi-i/MoDiCF.

The shuffle model of differential privacy provides promising privacy-utility balances in decentralized, privacy-preserving data analysis. However, the current analyses of privacy amplification via shuffling lack both tightness and generality. To address this issue, we propose the variation-ratio reduction as a comprehensive framework for privacy amplification in both single-message and multi-message shuffle protocols. It leverages two new parameterizations: the total variation bounds of local messages and the probability ratio bounds of blanket messages, to determine indistinguishability levels. Our theoretical results demonstrate that our framework provides tighter bounds, especially for local randomizers with extremal probability design, where our bounds are exactly tight. Additionally, variation-ratio reduction complements parallel composition in the shuffle model, yielding enhanced privacy accounting for popular sampling-based randomizers employed in statistical queries (e.g., range queries, marginal queries, and frequent itemset mining). Empirical findings demonstrate that our numerical amplification bounds surpass existing ones, conserving up to 30% of the budget for single-message protocols, 75% for multi-message ones, and a striking 75%-95% for parallel composition. Our bounds also result in a remarkably efficient Õ ( n ) algorithm that numerically amplifies privacy in less than 10 seconds for n = 10 8 users.

Local Differential Privacy (LDP) is now widely adopted in large-scale systems to collect and analyze sensitive data while preserving users’ privacy. However, almost all LDP protocols rely on a semi-trust model where users are curious-but-honest, which rarely holds in real-world scenarios. Recent works [6], [11], [62] show poor estimation accuracy of many LDP protocols under malicious threat models. Although a few works have proposed some countermeasures to address these attacks, they all require prior knowledge of either the attacking pattern or the poison value distribution, which is impractical as they can be easily evaded by the attackers.In this paper, we adopt a general opportunistic-and-colluding threat model and propose a multi-group Differential Aggregation Protocol (DAP) to improve the accuracy of mean estimation under LDP. Different from all existing works that detect poison values on individual basis, DAP mitigates the overall impact of poison values on the estimated mean. It relies on a new probing mechanism EMF (i.e., Expectation-Maximization Filter) to estimate features of the attackers. In addition to EMF, DAP also consists of two EMF post-processing procedures (EMF* and CEMF*), and a group-wise mean aggregation scheme to optimize the final estimated mean to achieve the smallest variance. Extensive experimental results on both synthetic and real-world datasets demonstrate the superior performance of DAP over state-of-the-art solutions.

Social trust assessment that characterizes a pairwise trustworthiness relationship can spur diversified applications. Extensive efforts have been put in exploration, but mainly focusing on applying graph convolutional network to establish a social trust evaluation model, overlooking user feature factors related to context-aware information on social trust prediction. In this article, we aim to design a new trust assessment framework GATrust which integrates multi-aspect properties of users, including user context-specific information, network topological structure information, and locally-generated social trust relationships. GATrust can assigns different attention coefficients to multi-aspect properties of users in online social networks, for improving the prediction accuracy of social trust evaluation. The framework can then learn multiple latent factors of each trustor-trustee pair to establish a social trust evaluation model, by fusing graph attention network and graph convolution network. We conduct extensive experiments on two popular real-world datasets and the results exhibit that our proposed framework can improve the precision of social trust prediction, outperforming the state-of-the-art in the literature by 4.3% and 5.5% on both two datasets, respectively.

Jointly learning from multiple datasets can help building versatile intelligent systems yet may give rise to serious concerns of data privacy and model selection. Specifically, on the one hand, these datasets can be distributed at various local clients, who may not be willing or do not ought to share data with each other. On the other hand, it is unrealistic to choose a model architecture that can well suit the disparate patterns and distributions carried by the various datasets in a priori. Whereas many works in federated learning [1] and neural architecture search [2] have been proposed to address one of the two concerns, very few have attempted the both. To close the gap, in this paper we deliver a framework, termedMulti-Granular Federated Neural Architecture Search(MGFNAS), to enable the automation of model architecture search in a federated and thus privacy-preserved setting. We argue that our MGFNAS framework is general in the sense that it does not impose any restriction on the search space or strategy, such that most existing neural architecture search techniques can be readily implemented in. The main idea of our framework is to search the optimal neural network architecture in two levels of granularity, enabling the neural-operator-basedmicro-levelsearch and the cell-basedmacro-levelsearch. The main challenge of implementing our framework lies in the fact that, due to the decentralized nature, the local architectures searched by multiple clients can differ drastically in order to fit their own datasets, while a general method to form the global model by aggregating the local architectures in both micro and macro levels is missing. To solve the issue, we propose a novel aggregation function, named Network Architecture Probabilistic Aggregation (NAPA). The key idea of our NAPA function is to treat the network architectures as graphs, of which the sub-graph structures being frequently appeared across multiple clients are modeled by probabilistic distributions. At each round, a global model is formed by sampling from those distributions in an exploration-exploitation fashion. Extensive experiments are carried out, and the results substantiate the viability and effectiveness of our proposed framework.

A contrastive distortion-level learning-based no-reference image-quality assessment (NR-IQA) framework is proposed in this study to further effectively model various distortion types with the same or different distortion levels. The proposed method aims to improve the prediction accuracy of NR-IQA. The proposed method consists of three parts: multiscale distortion-level representation learning, single-image NR-IQA, and a representation affinity module, which can reduce NR-IQA computational complexity while maintaining a low-distortion representation of high-distortion inputs. The proposed NR-IQA method aims to extract distributional features of samples in real distorted images and predict ambiguity based on distortion-level learning. Experimental results show that by comparing on many NR-IQA data sets the proposed method can outperform state-of-the-art methods.

Homomorphic encryption (HE) schemes, such as fully homomorphic encryption (FHE), support a number of useful computations on ciphertext in a broad range of applications, such as e-voting, private information retrieval, cloud security, and privacy protection. While FHE schemes do not require any interaction during computation, the key limitations are large ciphertext expansion and inefficiency. Thus, to overcome these limitations, we develop a novel cryptographic tool, MAS-Encryption (MASE), to support real-value input and secure computation on the multiply-add structure. The multiply-add structures exist in many important protocols, such as classifiers and outsourced protocols, and we will explain how MASE can be used to protect the privacy of these protocols, using two case study examples. Specifically, the first case study example is the privacy-preserving Naive Bayes classifier that can achieve minimal Bayes risk, and the other example is the privacy-preserving support vector machine. We prove that the constructed classifiers are secure and evaluate their performance using real-world datasets. Experiments show that our proposed MASE scheme and MASE based classifiers are efficient, in the sense that we achieve an optimal tradeoff between computation efficiency and communication interactions. Thus, we avoid the inefficiency of FHE based paradigm.

Networks that constantly transmit information and change structure are becoming increasingly prevalent. However, traditional privacy models are designed to protect static information, such as records in a database or a person’s profile information, which seldom changes. This conflict between static models and dynamic environments is dramatically hindering the effectiveness and efficiency of privacy preservation in today’s dynamic world. Hence, in this paper, we formally define the concept of dynamic privacy, present two novel perspectives, privacy propagation and accumulation, on the way private information can spread through dynamic cyberspace, and develop associated theories and mechanisms for preserving privacy in advanced complex networks, such as social networking sites where data are constantly being released, shared, and exchanged.

The primitive of verifiable database (VDB) can enable a resource-limited client to securely outsource an encrypted database to an untrusted cloud server and the client could efficiently retrieve and update the data at will. Meanwhile, the client can undoubtedly detect any misbehavior by the server if the database has been tampered with. We argue that most of the existing VDB schemes can only support the updating operation of replacement, rather than other common updating operations such asinsertionanddeletion. Recently, the first publicly verifiable VDB schemes that supports all updating operations was proposed based on the idea of hierarchical vector commitment. However, one disadvantage of the proposed VDB scheme is that the computation and storage complexity increases linearly when the client continually inserts data records in the same index of the database. As a result, it remains an open problem how to construct an efficient (and publicly verifiable) VDB scheme that can support all updating operations regardless of the manner of insertion. In this paper, we first introduce a new primitive called committed invertible Bloom filter (CIBF) and utilize it to propose a new publicly verifiable VDB scheme that can support all kinds of updating operations. Additionally, the proposed construction is efficient regardless of the manner of updating operations and thus provides an affirmative answer to the above open problem.

Evasion attack on discrete data is a challenging, while practically interesting research topic. It is intrinsically an NP-hard combinatorial optimization problem. Characterizing the conditions guaranteeing the solvability of an evasion attack task thus becomes the key to understand the adversarial threat. Our study is inspired by the weak submodularity theory. We characterize the attackability of a targeted classifier on discrete data in evasion attack by bridging the attackability measurement and the regularity of the targeted classifier. Based on our attackability analysis, we propose a computationally efficient orthogonal matching pursuit-guided attack method for evasion attack on discrete data. It provides provably computational efficiency and attack performances. Substantial experimental results on real-world datasets validate the proposed attackability conditions and the effectiveness of the proposed attack method.

Encryption alone is not enough to protect data privacy, because access pattern leaks some sensitive information. Oblivious RAM (ORAM), the solution to this problem, is still far from practical deployment for heavy storage and communication/computation overhead. To reduce them, an insightful idea was proposed to utilize non-colluding clouds to shift client computation and client-cloud communication to the clouds. The proposed multi-cloud ORAM achieved O(1) client-cloud bandwidth cost and removed most of client computation. In this paper, we exploit “disconnected ORAMoperation” and design “two-layerencryption” to further reduce these overheads. Experiments show that our proposed scheme, NewMCOS, significantly reduces evict cache size from GB/MB to KB level with about 2-3 times lower response time and 20 percent savings in bandwidth for clouds, compared to other schemes. Theoretically speaking, we reduce evict cache size from O(√N) to O(ZK), where N is the number of real data blocks, K is the number of clouds (2 <; K <; <; √N ), and Z is the number of real blocks uploaded from the client for eviction. By employing “lazy eviction operation”, the write frequency is reduced by O(Z), the shuffling bandwidth cost is reduced by Ω(Z log Z). Meanwhile, NewMCOS is proved to be secure.

The rich cloud services in the Internet of Things create certain needs for edge computing, in which devices should be able to handle storage tasks securely, reliably, and efficiently. When processing the storage requests from edge devices, each cloud server is supposed to eliminate duplicate copies of repeating data to reduce the amount of storage space and save on bandwidth. To protect data confidentiality while supporting deduplication, some convergent-encryption-based techniques have been proposed to encrypt the data before uploading. However, all these works cannot meet two requirements while preventing brute-force attacks: (i) power-constrained edge nodes should update encryption keys efficiently when an edge node is abandoned; and (ii) the access privacy of edge nodes should be guaranteed. In this article, we propose a novel encryption scheme for secure chunk-level deduplication. Based on this scheme, we present two constructions of the secure deduplication system that support an efficient key update protocol. The key update protocol does not involve any edge node in computational tasks, so that the deduplication system can adopt an active key update strategy. Moreover, one of our constructions, which is called advance construction, can provide access privacy assurances for edge nodes. The security analysis is given in terms of the proposed threat model. The experimental analysis demonstrates that the proposed deduplication system is practical.