Secure manifold learning on datasets distributed among multiple data owners can benefit or even spawn many applications. For example, multiple service providers can jointly fit low-dimensional embeddings of their users' network behavior data to improve the accuracy of anomaly detection while addressing their privacy concerns about the datasets. In this paper, we focus on a classic manifold learning technique, known as isometric mapping (Isomap), and propose SIsomap, the first secure, distributed manifold learning system. We construct SIsomap based on secret sharing techniques and introduce careful optimizations. In particular, we propose two communication-efficient secure building blocks that focus on top-k and all-pairs shortest paths computation, respectively, and reduce secure operations by leveraging the characteristics of Isomap. Experimental results on both synthetic and real-world datasets demonstrate that our secure top-k and all-pairs shortest paths protocols are respectively up to 13.6× and 1818.5× faster than the state-of-the-art methods, and SIsomap as a whole is 11.1× to 28.8× faster than the baseline solution.

Private Set Intersection (PSI) allows two mutually distrusting parties to compute the intersection of their sets without revealing any additional information, and has found numerous applications. A part of applications require labeled PSI in the unbalanced setting, where a server holds a label for each item in a set that is much larger than the set held by a client, and the client obtains the intersection and the corresponding labels. In this paper, we present a new concretely efficient labeled PSI protocol in the unbalanced setting, without using computation-heavy homomorphic encryption. Our protocol is based on Distributed Point Function (DPF) with hardware acceleration from fixed-key AES-NI, and has communication complexity linear in the size of a small set of the client and sublinear in the size of a large set of the server. Our protocol exploits two Oblivious Pesudorandom Function (OPRF) protocols, based on Diffle-Hellman PRFs or block ciphers, to achieve a trade-off between computation and communication. Our implementation demonstrates that our protocol outperforms the previous labeled and unbalanced PSI protocols. In particular, for two sets with respective$2^{24}$and 1 items, where each item has a 32-byte label, our protocol takes 1.19 seconds for an end-to-end performance, resulting in$26 \times $improvement compared to the state-of-the-art protocol by Cong et al. (CCS 2021). In terms of the cost of the one-time initialization, we speed up the computations more than$325\times $in the above comparison.

Privacy-preserving collaborative data analysis is a popular research direction in recent years. Among all such analysis tasks, privacy-preserving SQL queries on multi-party databases are of particular industrial interest. Although the privacy concern can be addressed by many cryptographic tools, such as secure multi-party computation (MPC), the efficiency of executing such SQL queries is far from satisfactory, especially for high-volume databases. In particular, existing MPC-based solutions treat each SQL query as an isolated task and launch it from scratch, in spite of the nature that many SQL queries are done regularly and somewhat overlap in their functionalities. In this work, we are motivated to exploit this nature to improve the efficiency of MPC-based, privacy-preserving SQL queries. We introduce a cache-like optimization mechanism. To ensure a higher cache hit rate and reduce redundant MPC operators, we present a cache structure different from that of plain databases and design a set of cache strategies. Our optimization mechanism, SMPCache, can be built upon secret-sharing-based MPC frameworks, which attract much attention from the industry. To demonstrate the utility of SMPCache, we implement it on Rosetta, an open-source MPC library, and use real-world datasets to launch extensive experiments on some basic SQL operators (e.g., Filter, Order-by, Aggregation, and Inner-Join) and some representative composite SQL queries. To give a data point, we note that SMPCache can achieve most up to 3536× efficiency improvement on the TPC-DS dataset and 562× on the TPC-H dataset at a moderate storage cost. We also apply SMPCache to the basic SQL operators (Filter, Order-by, Group-by, Aggregation, and Inner-join) of the Secrecy framework, achieving up to 127.3× efficiency improvement.

Secure Multi-party Computation (MPC) provides a promising solution for privacy-preserving multi-source data analytics. However, existing MPC-based collaborative analytics systems (MCASs) have unsatisfying performance for scenarios with dynamic databases. Naively running an MCAS on a dynamic database would lead to significant redundant costs and raise performance concerns, due to the substantial duplicate contents between the pre-updating and post-updating databases.

Secure two-party computation (2PC) in the RAM model has attracted huge attention in recent years. Most existing results only support semi-honest security, with the exception of Keller and Yanai (Eurocrypt 2018) with very high cost. In this paper, we propose an efficient RAM-based 2PC protocol with active security and one-bit leakage.1)We propose an actively secure protocol for distributed point function (DPF), with one-bit leakage, that is essentially as efficient as the state-of-the-art semi-honest protocol. Compared with previous work, our protocol takes about 50× less communication for a domain with 220entries, and no longer requires actively secure generic 2PC.2)We extend the dual-execution protocol to allow reactive computation, and then build a RAM-based 2PC protocol with active security on top of our new building blocks. The protocol follows the paradigm of Doerner and shelat (CCS 2017). We are able to prove that the protocol has end-to-end one-bit leakage.3)Our implementation shows that our protocol is almost as efficient as the state-of-the-art semi-honest RAM-based 2PC protocol, and is at least two orders of magnitude faster than prior actively secure RAM-based 2PC without leakage, providing a realistic trade-off in practice.

Federated learning allows a large number of resource-constrained clients to train a globally-shared model together without sharing local data. These clients usually have only a few classes (categories) of data for training, where the data distribution is non-iid (not independent identically distributed). In this article, we put forward the concept ofcategory privacyfor the first time to indicatewhich classes of data a client has, which is an important but ignored privacy goal in the federated learning with non-iid data. Although secure aggregation protocols are designed for federated learning to protect the input privacy of clients, we perform the first systematic study oncategory inference attackand demonstrate that these protocols cannot fully protect category privacy. We design a differential selection strategy and two de-noising approaches to achieve the attack goal successfully. In our evaluation, we apply the attack to non-iid federated learning settings with various datasets. On MNIST, CIFAR-10, AG_news, and DBPedia dataset, our attack achieves$>90\%$accuracy measured in F1-score in most cases. We further consider a possible detection method and propose two strategies to make the attack more inconspicuous.

Big data, due to its promotion for industrial intelligence, has become the cornerstone of the Industry 4.0 era.Federated learning, proposed by Google, can effectively integrate data from different devices and different domains to train models under the premise of privacy preservation. Unfortunately, this new training paradigm faces security risks both on the client side and server side. This article proposes a new federated learning scheme to defend from client-side malicious uploads (e.g., backdoor attacks). In addition, we use cryptography techniques to prevent server-side privacy attacks (e.g., membership inference). Thesecure partial aggregationprotocol we designed improves the privacy and robustness of federated learning. The experiments show that models can achieve high accuracy of over 90% with a proper upload proportion, while the accuracy of the backdoor attack decreased from 99.5% to 0% with the best result. Meanwhile, we prove that our protocol can disable privacy attacks.

The federated learning provides an effective solution to train collaborative models over a large scale of participated Industrial Internet of Things (IIoT) applications with the help of a global server, building an intelligent life. However, the federated learning is vulnerable to the backdoor attack from strong malicious participants. The backdoor attack is inconspicuous and may result in devastating consequences. To resist the attack on IIoT applications, we propose the federated backdoor filter defense that can identify backdoor inputs and restore the data to availability by theblur-label-flippingstrategy. We build multiple filters with eXplainable AI models on the server and send them to clients randomly, preventing advanced attackers from evading the defense. Our backdoor filters show significant backdoor recognition with the accuracy up to 99%. After the implementation of the blur-label-flipping strategy, victim's local model on suspicious backdoor samples can achieve the accuracy up to 88%.

Federated learning (FL) enables a large number of clients to collaboratively train a global model through sharing their gradients in each synchronized epoch of local training. However, a centralized server used to aggregate these gradients can be compromised and forge the result in order to violate privacy or launch other attacks, which incurs the need to verify the integrity of aggregation. In this work, we explore how to design communication-efficient and fast verifiable aggregation in FL. We propose VeriFL, a verifiable aggregation protocol, with O(N) (dimension-independent) communication and O(N+ d) computation for verification in each epoch, where N is the number of clients and d is the dimension of gradient vectors. Since d can be large in some real-world FL applications (e.g., 100K), our dimension-independent communication is especially desirable for clients with limited bandwidth and high-dimensional gradients. In addition, the proposed protocol can be used in the FL setting where secure aggregation is needed or there is a subset of clients dropping out of protocol execution. Experimental results indicate that our protocol is efficient in these settings.