Deep Neuro-Fuzzy Inference Systems (DNFIS) seamlessly fuse neural networks with the fuzzy inference system enabling intricate decision-making and knowledge representation, while upholding a commendable degree of adaptability and interpretability. However, the challenge of privacy-preserving inference (PI) over DNFIS has remained largely uncharted, with no prior research addressing this critical issue. In this paper, we embark on an exploration of this issue. We introduce an efficient and secure PI framework for DNFIS, named PrivDNFIS, which leverages the post-quantum lattice-based homomorphic encryption to implement secure computation protocols for PI over DNFIS. Our work incorporates several non-trivial performance enhancements. Firstly, it consolidates multiple elements of input feature vectors into a single message, reducing encryption/decryption overhead. Secondly, building upon this novel encoding approach, PrivDNFIS can perform ciphertext aggregation and vector-vector inner production without necessitating time-consuming ciphertext rotation operations. Thirdly, we replace the softmax function in the DNFIS layer with a quadratic function to further enhance inference efficiency, without compromising the inference accuracy. Under the given threat model, we provide formal security proof for PrivDNFIS. In comprehensive experimental results, PrivDNFIS demonstrates an approximately 1.9 to 4.4 times reduction in end-to-end time cost compared to the benchmark.

While decentralized authentication mechanisms have gained significant attention for enabling user-centric identity management without centralized authorities, the critical counterpart - authenticated key exchange (AKE) in decentralized settings - remains understudied. Although it forms the basis for secure communication in decentralized scenarios, shifting existing AKE protocols to decentralized settings is impractical: the trust assumption is different, and the insufficient support for dynamic identity attributes, etc. To address these challenges, we present a novel decentralized AKE protocol that innovatively integrates attribute authentication with key exchange through multi-party secure computation. Building upon MPCAuth's foundational framework (S&P 23), our protocol goes further to provide key exchange based on authentication of real-world attributes such as a digital passport and email address, etc. Our protocol establishes a new paradigm for decentralized AKE without complex credential operations and heavy zero-knowledge proof. The core of our protocol is a distributed way to securely reconstruct the attributes and establish a session key. We further evaluate its performance across multiple servers. Experimental results on servers under 5 demonstrate that it can finish the full AKE procedure in an acceptable time, enabling efficient and scalable multi-party key AKE in distributed environments.

Geo-Indistinguishability (GI) is a powerful privacy model that can effectively protect location information by limiting the ability of an attacker to infer a user's true location. In real life, locations usually have different sensitive levels in terms of privacy; for example, shopping malls might be low-sensitive while home addresses might be high-sensitive for users. But the GI model does not consider the various sensitive levels of locations, and implements the same perturbation on all locations to meet the highest privacy requirement. This would cause overprotection of low-sensitive locations and reduce data utility. To strike a good balance between privacy and utility, in this paper, we propose a novel privacy notion, termedLocation-DiscriminativeGeo-Indistinguishability (LDGI), which takes into account different sensitive levels of location privacy. With LDGI model, we then develop a perturbation scheme called EM-LDGI based on the exponential mechanism, and an advance scheme MinQL to further enhance data utility. To improve the efficiency of the proposed schemes, we design a scheme MinQL-S with the assistance of the spanner graph, at the cost of a slight utility degradation. We theoretically analyze that the proposed schemes satisfy LDGI and evaluate their performance by extensive experiments on both synthetic and real datasets. The comparison with GI mechanisms demonstrates the advantages of the LDGI model.

With the rapid development of social networks, the harm caused by the leakage of personal sensitive information is becoming increasingly serious.In order to detect and identify personal sensitive information, existing methods build matching rules to detect specific sensitive entities and use machine learning methods to classify sensitive text.These methods face challenges in context analysis and adapting to Chinese language characteristics.This paper proposes CPSID, a method for detecting Chinese personal sensitive information.On the one hand, CPSID utilises rule matching to detect specific personal sensitive information only containing letters and numbers.More importantly, CPSID constructs a sequence labelling model named EBC (ELECTRA-BiLSTM-CRF) to detect more complex personal sensitive information that consist of Chinese characters.The EBC model uses the latest ELECTRA algorithm to implement word embedding, and uses BiLSTM and CRF models to extract personal sensitive information, which can detect Chinese sensitive entities accurately by analysing context information.The model achieves an F1 score of 94.09% on Chinese datasets, outperforming other similar models.Additionally, experiments on real data show CPSID has a better detection result than individual methods (rule matching or sequence labelling).

Network packets record communication behaviors and details, which is important for security audits, attack detection, and forensic analysis. For the effectiveness and timeliness of security analysis, it is necessary to fully store network packets and build an efficient packet index. However, the existing packet indexing algorithms based on the radix tree ignore the distribution characteristics of network traffic and use internal nodes with the same capacity for index construction, resulting in wasted disk space and poor retrieval performance. As a solution,$w$e propose ANTI, an adaptive network traffic indexing algorithm similar to Adaptive Radix Tree, which can adaptively switch internal nodes with different capacity according to the density of network traffic and compress the common prefix and distinct suffix of traffic attributes to balance the index construction performance and space utilization. We also implement a packet-aware network traffic archiving and indexing system to achieve full packet archival, efficient indexing, and fast retrieval. Finally, we empirically evaluate ANTI in IPv4 (IPv6) traffic scenarios, and the results confirm the effectiveness of ANTI as well as the benefit of adopting ANTI for enhancing indexing and retrieval performance compared with other state-of-art algorithms.

Computing the quantile of a massive data stream has been a crucial task in networking and data management. However, existing solutions assume a centralized model where one data owner has access to all data. In this paper, we put forward a study of secure quantile aggregation betweenprivatedata streams, where data streams owned by different parties would like to obtain a quantile of the union of their data without revealing anything else about their inputs. To this end, we designed efficient cryptographic protocols that are secure in the semi-honest setting as well as the malicious setting. By incorporating differential privacy, we further improve the efficiency by 1.1× to 73.1×. We implemented our protocol, which shows practical efficiency to aggregate real-world data streams efficiently.

The extensive data collection performed by the Internet of Things (IoT) devices can put users at risk of data leakage. Consequently, IoT vendors are legally obliged to provide privacy policies to declare the scope and purpose of the data collection. However, complex and lengthy privacy policies are unfriendly to users, and the lack of a machine-readable format makes it difficult to check policy compliance automatically. To solve these problems, we first put forward a purpose-aware rule to formalize the purpose-driven data collection or use statement. Then, a novel approach to identify the rule from natural language privacy policies is proposed. To address the issue of diversity of purpose expression, we present the concepts of explicit and implicit purpose, which enable using the syntactic and semantic analyses to extract purposes in different sentences. Finally, the domain adaption method is applied to the semantic role labeling (SRL) model to improve the efficiency of purpose extraction. The experiments that are conducted on the manually annotated dataset demonstrate that this approach can extract purpose-aware rules from the privacy policies with a high recall rate of 91%. The implicit purpose extraction of the adapted model significantly improves the F1-score by 11%.

Proxy re-encryption (PRE) provides a promising solution for encrypted data sharing in public cloud. When data owner Alice is going to share her encrypted data with data consumer Bob, Alice generates a re-encryption key and sends it to the cloud server (proxy); by using it, the proxy can transform Alice's ciphertexts into Bob's without learning anything about the underlying plaintexts. Despite that existing PRE schemes can prevent the proxy from recovering Alice's secret key by collusion attacks with Bob, due to the inherent functionality of PRE, it is inevitable that the proxy and Bob together are capable to gain and distribute Alices decryption capabilities. Even worse, the malicious proxy can deny that it has leaked the decryption capabilities and has very little risk of getting caught. To tackle this problem, we introduce the concept of Accountable Proxy Re-Encryption (APRE), whereby if the proxy is accused to abuse the re-encryption key for distributing Alice's decryption capability, a judge algorithm can decide whether it is innocent or not. We then present a non-interactive APRE scheme and prove its CPA security and accountability under DBDH assumption in the standard model. Finally, we show how to extend it to a CCA secure one.

Correlated oblivious transfer (COT) is a crucial building block for secure multi-party computation (MPC) and can be generated efficiently via OT extension. Recent works based on the pseudorandom correlation generator (PCG) paradigm presented a new way to generate random COT correlations using only communication sublinear to the output length. However, due to their high computational complexity, these protocols are only faster than the classical IKNP-style OT extension under restricted network bandwidth. In this paper, we propose new COT protocols in the PCG paradigm that achieve unprecedented performance. \em With $50$ Mbps network bandwidth, our maliciously secure protocol can produce one COT correlation in $22$ nanoseconds. More specifically, our results are summarized as follows: \beginenumerate \item We propose a semi-honest COT protocol with sublinear communication and linear computation. This protocol assumes primal-LPN and is built upon a recent VOLE protocol with semi-honest security by Schoppmann et al. (CCS 2019). We are able to apply various optimizations to reduce its communication cost by roughly $15\times$, not counting a one-time setup cost that diminishes as we generate more COT correlations. \item We strengthen our COT protocol to malicious security with no loss of efficiency. Among all optimizations, our new protocol features a new checking technique that ensures correctness and consistency essentially for free. In particular, our maliciously secure protocol is only \em $1-3$ nanoseconds slower for each COT. \item We implemented our protocols, and the code will be publicly available at EMP toolkit. We observe at least $9\times$ improvement in running time compared to the state-of-the-art protocol by Boyle et al. (CCS 2019) in both semi-honest and malicious settings under any network faster than $50$ Mbps. \endenumerate With this new record of efficiency for generating COT correlations, we anticipate new protocol designs and optimizations will flourish on top of our protocol.

OAuth 2.0 is one of the most widely used Internet protocols for authorization/single sign-on (SSO) and is also the foundation of the new SSO protocol OpenID Connect. Due to its complexity and its flexibility, it is difficult to comprehensively analyze the security of the OAuth 2.0 standard, yet it is critical to obtain practical security guarantees for OAuth 2.0. In this paper, we present the first computationally sound security analysis of OAuth 2.0. First, we introduce a new primitive, the three-party authenticated secret distribution (3P-ASD for short) protocol, which plays the role of issuing the secret and captures the token issue process of OAuth 2.0. As far as we know, this is the first attempt to formally abstract the authorization technology into a general primitive and then define its security. Then, we present a sufficiently rich three-party security model for OAuth protocols, covering all kinds of authorization flows, providing reasonably strong security guarantees and moreover capturing various web features. To confirm the soundness of our model, we also identify the known attacks against OAuth 2.0 in the model. Furthermore, we prove that two main modes of OAuth 2.0 can achieve our desired security by abstracting the token issue process into a 3P-ASD protocol. Our analysis is not only modular which can reflect the compositional nature of OAuth 2.0, but also fine-grained which can evaluate how the intermediate parameters affect the final security of OAuth 2.0.

Data transmission exists in almost all the Internet-based applications, while few of them consider the property of nonrepudiation as part of data security. If a data transmission scheme is performed without the endorsement of a trusted third party (TTP) or a central server, it is easy to raise disputes while transmitting valuable data, especially digital goods, because a dishonest participant can deny the fact of particular data transmission instance. The above problem can be solved by signing and encrypting. However, digital signature schemes usually assume public key infrastructure (PKI), increasing the burden on certificate management and are not suitable for distributed networks without TTP such as blockchain. To solve the above problems, we propose two new schemes for nonrepudiation data transmission based on blockchain (we call it BNRDT): one for short message transmission and the other for large file transmission. In BNRDT schemes, nonrepudiation evidence of data transmission is generated and stored on the blockchain to satisfy both the properties of nonrepudiation (including nonrepudiation of origin and nonrepudiation of receipt) and data confidentiality. We implement and test the schemes on Hyperledger Fabric. The experimental results show that the proposed schemes can provide appealing performance.

Transport Layer Security (TLS) is one of the most widely used Internet protocols for secure communications. TLS 1.3, the next-generation protocol, is currently under development, with the latest candidate being draft-18. For flexibility and compatibility, TLS supports various ciphersuites and offers configurable selection of multiple protocol versions, which unfortunately opens the door to practical attacks. For example, although TLS 1.3 is now proven secure separately, coexisting with previous versions may be subject to backwards compatibility attacks. In this paper, we present a formal treatment of the multi-ciphersuite and backwards-compatibility security of TLS 1.3 (specifically, draft-18). We introduce a multi-stage security model, covering all known kinds of compositional interactions (w.r.t. ciphersuites and protocol versions) and reasonably strong security notions. Then we dissect the cross-ciphersuite attack regarding TLS 1.2 in our model, and show that the TLS 1.3 handshake protocol satisfies the multi-ciphersuite security, highlighting the strict necessity of including more information in the signature. Furthermore, we demonstrate how the backwards compatibility attack by Jager et al. can be identified owing to our model, and prove that the handshake protocol can achieve our desired strong security if certain countermeasures are adopted. Our treatment is also applicable to analyzing other protocols.