Content providers increasingly utilise Content Delivery Networks (CDNs) to enhance users' content download experience. However, this deployment scenario raises significant security concerns regarding content confidentiality and user privacy due to the involvement of third-party providers. Prior proposals using private information retrieval (PIR) and oblivious RAM (ORAM) have proven impractical due to high computation and communication costs, as well as integration challenges within distributed CDN architectures. In response, we present \textsf{OblivCDN}, a practical privacy-preserving system meticulously designed for seamless integration with the existing real-world Internet-CDN infrastructure. Our design strategically adapts Range ORAM primitives to optimise memory and disk seeks when accessing contiguous blocks of CDN content, both at the origin and edge servers, while preserving both content confidentiality and user access pattern hiding features. Also, we carefully customise several oblivious building blocks that integrate the distributed trust model into the ORAM client, thereby eliminating the computational bottleneck in the origin server and reducing communication costs between the origin server and edge servers. Moreover, the newly-designed ORAM client also eliminates the need for trusted hardware on edge servers, and thus significantly ameliorates the compatibility towards networks with massive legacy devices.In real-world streaming evaluations, OblivCDN} demonstrates remarkable performance, downloading a $256$ MB video in just $5.6$ seconds. This achievement represents a speedup of $90\times$ compared to a strawman approach (direct ORAM adoption) and a $366\times$ improvement over the prior art, OblivP2P.

This paper applies Explainable Artificial Intelligence (XAI) to improve the interpretability of neural differential cryptanalysis on the SPECK cipher. We use Local Interpretable Model-agnostic Explanations (LIME) to analyse and visualise feature importance in neural distinguishers, giving signed contributions and absolute rankings. Signed contributions show whether, and how strongly, specific bit positions influence the model's decision, while absolute rankings reflect their importance regardless of sign. To study interactions beyond single bits, we introduce a Systematic Masking Approach to reveal relations among bits by testing if chosen combinations of masked bits alter classification accuracy. On Gohr's 8-round SPECK32/64 distinguisher, masking up to four-bit combinations shows that decisions involve multi-bit interactions rather than isolated single-bit effects. Although LIME highlights strong single-bit signals, masking reveals interaction patterns consistent with differential cryptanalysis. These findings clarify model behaviour in neural cryptanalysis and show XAI's value for exposing and visualising interaction structure in ciphertext features and decisions.

As applications increasingly rely on vector search to find semantically similar content in large-scale databases, preserving user query privacy is of paramount importance. Existing solutions based on advanced cryptography, such as Fully Homomorphic Encryption (FHE) or Private Information Retrieval (PIR), often incur prohibitive computational or communication overheads, limiting their practical deployment. This paper introduces PrivANN, a fully oblivious system for private approximate nearest neighbor (ANN) search that leverages Trusted Execution Environments (TEEs). PrivANN employs a read-optimized Oblivious RAM (ORAM) protocol to defend against side-channel leakage, introduces a novel shuffling mechanism that decouples costly offline preparation from fast online operations and incorporates a novel oblivious Top-k selection algorithm. We formally prove PrivANN’s security guarantees and demonstrate its real-world performance. Our evaluation shows that PrivANN improves throughput by 2.4x over state-of-the-art FHE-based systems while achieving superior search quality, and reduces client-side communication overhead from gigabytes to kilobytes compared to PIR-based approach.

Recent developments in the field of Dynamic Searchable Symmetric Encryption (DSSE) with forward and backward privacy have attracted much attention from both research and industrial communities. However, most DSSE schemes with forward and backward privacy schemes only support single keyword queries, which impedes its prevalence in practice. Although some forward and backward private DSSE schemes with expressive queries (e.g., conjunctive queries) have been introduced, their backward privacy either essentially corresponds to single keyword queries or forward privacy is not comprehensive. In addition, the deletion of many DSSE schemes is achieved by addition paired with a deletion mark (i.e., lazy deletion). To address these problems, we present two novel DSSE schemes with conjunctive queries (termed SDSSE-CQ and SDSSE-CQ-S), which achieve both forward and backward privacy. To analyze their security, we present two new levels of backward privacy (named Type-O and Type-O-, more and more secure), which give a more comprehensive understanding of the leakages of conjunctive queries in the OXT framework. Eventually, the security analysis and experimental evaluations show that the proposed schemes achieve better security with reasonable computation and communication increase.

At CRYPTO'19, Gohr[1] presented ResNet-based neural distinguishers (ND) for the round-reduced SPECK32/64 cipher. However, due to the black-box use of such deep learning models, it is hard for humans to understand why these distinguishers work, impeding advancements in cryptanalytic knowledge. In this work, we aim to effectively adapt eXplainable Artificial Intelligence (XAI) techniques, notably Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP), to gain a detailed understanding of the important features useful in Gohr's neural distinguishers.

The rapid progress in the field of IoT and its wide-ranging applications emphasize the criticality of robust security measures for effectively sharing, storing, and managing sensitive data generated by IoT devices. Regulations such as the Consumer Data Rights (CDR) highlight the need for the seamless sharing of sensitive data with authorized third parties while ensuring confidentiality and privacy. To enable such secure sharing, a data storage and sharing scheme should fulfill the following core requirements: (a) support multi-client data sharing settings, allowing IoT data owners to authorize multiple clients; (b) a dynamic storage environment permitting IoT owners to add or remove files with minimal privacy leak; (c) decentralized storage for distributing data across servers or Cloud Service Providers (CSPs) for greater security; and (d) efficient privilege revocation mechanism which incurs less computation and communication overhead. To address these requirements, we have proposed a novel keyword search scheme using computationally lightweight cryptographic primitives. Our scheme empowers IoT data owners to securely share, store and manage encrypted data in the CSPs, providing better security and privacy. We have provided formal security proof for our scheme as well as validated its efficiency via extensive experiments on the Docker platform. On a database of 12 million keyword/document pairs (with 105 documents and 103 keywords), our scheme took about 18 ms to return all matched documents.

Reputation systems are widely used to provide a trustworthy environment and improve the sustainability of online discussions. They help users understand and evaluate the quality of information by collecting and counting feedback from different users. However, a common issue in most reputation systems is how to maintain users’ reputation and protect their anonymity simultaneously. In this paper, we introduce a new practical anonymous reputation system based on SGX. The establishment of an anonymous reputation system has a positive effect on sustainable trust in reputation-based online applications. Our system achieves the combination of reputation and anonymity by utilizing Intel SGX and the Bloom filter. The Path ORAM algorithm is also implemented to resist side-channel attacks. The experiments demonstrate that our system achieves high performance in terms of computation and storage costs. When compared to two state-of-the-art anonymous reputation systems, our system has better computation performance with at least three orders of magnitude.

The clustering algorithm is a useful tool for analyzing medical data. For instance, the k-means clustering can be used to study precipitating factors of a disease. In order to implement the clustering algorithm efficiently, data computation is outsourced to cloud servers, which may leak the private data. Encryption is a common method for solving this problem. But cloud servers are difficult to calculate ciphertexts from multiple parties. Hence, we choose multi-key fully homomorphic encryption (FHE), which supports computations on the ciphertexts that have different secret keys, to protect the private data. In this paper, based on Chen's multi-key FHE scheme, we first propose secure squared euclidean, comparison, minimum, and average protocols. Then, we design the basic and advanced schemes for implementing the secure multi-party k-means clustering algorithm. In the basic scheme, the implementation of homomorphic multiplication includes the process of transforming ciphertexts under different keys. In order to implement homomorphic multiplication efficiently, the advanced scheme uses an improved method to transform ciphertexts. Meanwhile, almost all computations are completely outsourced to cloud servers. We prove that the proposed protocols and schemes are secure and feasible. Simulation results also show that our improved method is helpful for improving the homomorphic multiplication of Chen's multi-key FHE scheme.

Federated learning has recently emerged as a paradigm promising the benefits of harnessing rich data from diverse sources to train high quality models, with the salient features that training datasets never leave local devices. Only model updates are locally computed and shared for aggregation to produce a global model. While federated learning greatly alleviates the privacy concerns as opposed to learning with centralized data, sharing model updates still poses privacy risks. In this paper, we present a system design which offers efficient protection of individual model updates throughout the learning procedure, allowing clients to only provide obscured model updates while a cloud server can still perform the aggregation. Our federated learning system first departs from prior works by supporting lightweight encryption and aggregation, and resilience against drop-out clients with no impact on their participation in future rounds. Meanwhile, prior work largely overlooks bandwidth efficiency optimization in the ciphertext domain and the support of security against an actively adversarial cloud server, which we also fully explore in this paper and provide effective and efficient mechanisms. Extensive experiments over several benchmark datasets (MNIST, CIFAR-10, and CelebA) show our system achieves accuracy comparable to the plaintext baseline, with practical performance.

Driven by the cloud-first initiative taken by various governments and companies, it has become a common practice to outsource spatial data to cloud servers for a wide range of applications such as location-based services and geographic information systems. Searchable encryption is a common practice for outsourcing spatial data which enables search over encrypted data by sacrificing the full security via leaking some information about the queries to the server. However, these inherent leakages could equip the server to learn beyond what is considered in the scheme, in the worst-case allowing it to reconstruct of the database. Recently, a novel form of database reconstruction attack against such kind of outsourced spatial data was introduced (Markatou and Tamassia, IACR ePrint 2020/284), which is performed using common leakages of searchable encryption schemes, i.e., access and search pattern leakages. An access pattern leakage is utilized to achieve an order reconstruction attack, whereas both access and search pattern leakages are exploited for the full database reconstruction attack. In this paper, we propose two novel schemes for outsourcing encrypted spatial data supporting dynamic range search. Our proposed schemes leverage R+tree to partition the dataset and binary secret sharing to support secure range search. They further provide backward and content privacy and do not leak the access pattern, therefore being resilient against the above mentioned database reconstruction attacks. The evaluations and results on the real-world dataset demonstrate the practicality of our schemes, due to (a) the minimal round-trip between the client and server, and (b) the low computation and storage overhead on the client side.

Network Function Virtualisation (NFV) advances the adoption of composable software middleboxes. Accordingly, cloud data centres become major NFV vendors for enterprise traffic processing. Due to the privacy concern of traffic redirection to the cloud, secure middlebox systems (e.g., BlindBox) draw much attention; they can process encrypted packets against encrypted rules directly. However, most of the existing systems supporting pattern matching based network functions require the enterprise gateway to tokenise packet payloads via sliding windows. Such tokenisation induces a considerable communication overhead, which can be over 100× to the packet size. To overcome this bottleneck, in this article, we propose the first bandwidth-efficient encrypted pattern matching protocol for secure middleboxes. We resort to a primitive called symmetric hidden vector encryption (SHVE), and propose a variant of it, aka SHVE+, to achieve constant and moderate communication cost. To speed up, we devise encrypted filters to reduce the number of accesses to SHVE+ during matching highly. We formalise the security of our proposed protocol and conduct comprehensive evaluations over real-world rulesets and traffic dumps. The results show that our design can inspect a packet over 20 k rules within 100$\mu$s. Compared to prior work, it brings a saving of 94 percent in bandwidth consumption.

We propose a multi-client Symmetric Searchable Encryption (SSE) scheme based on the single-user protocol [3] . The scheme allows any user to generate a search query by interacting with any θ is a threshold parameter) `helping' users. It preserves the privacy of a database content against the server assuming a leakage of up to θ-1 users' keys to the server while hiding the query from the θ-1 `helping users'. To achieve the query privacy, we design a new distributed key-homomorphic pseudorandom function (PRF) that hides the PRF input (search keyword) from the `helping' users. We present a concrete construction of our randomizable distributed PRF. By distributing the utilized keys among the users, the need for a constant online presence of the data owner to provide services to the users is eliminated, while providing resilience against a user key exposure. We extended our scheme to support user revocation in two different scenarios. In addition, we give a solution for fast update of the encryption key with the overhead significantly smaller than the re-encryption and re-uploading the database. Moreover, our scheme is secure against passive and active collusion between the server and a subset of users.

Outlier detection is widely used in practice to track the anomaly on incremental data sets, such as network traffic and system logs. However, these data sets often involve sensitive information, and sharing the data to third parties for anomaly detection raises privacy concerns. In this article, we present a privacy-preserving outlier detection (PPOD) protocol for incremental data sets. The protocol decomposes the outlier detection algorithm into several phases and recognizes the necessary cryptographic operations in each phase. It realizes several cryptographic modules via efficient and interchangeable protocols to support the above cryptographic operations and composes them in the overall protocol to enable outlier detection over encrypted data sets. To support efficient updates, it integrates the sliding window model to periodically evict the expired data in order to maintain a constant update time. We build a prototype of PPOD and systematically evaluates the cryptographic modules and the overall protocols under various parameter settings. Our results show that PPOD can handle encrypted incremental data sets with a moderate computation and communication cost.

In this paper, we propose GraphSE\textsuperscript2, an encrypted graph database for online social network services to address massive data breaches. GraphSE\textsuperscript2 ~preserves the functionality of social search, a key enabler for quality social network services, where social search queries are conducted on a large-scale social graph and meanwhile perform set and computational operations on user-generated contents. To enable efficient privacy-preserving social search, GraphSE\textsuperscript2 ~provides an encrypted structural data model to facilitate parallel and encrypted graph data access. It is also designed to decompose complex social search queries into atomic operations and realise them via interchangeable protocols in a fast and scalable manner. We build GraphSE\textsuperscript2 ~with various queries supported in the Facebook graph search engine and implement a full-fledged prototype. Extensive evaluations on Azure Cloud demonstrate that GraphSE\textsuperscript2 ~is practical for querying a social graph with a million of users.

The recently proposed Oblivious Cross-Tags (OXT) protocol (CRYPTO 2013) has broken new ground in designing efficient searchable symmetric encryption (SSE) protocol with support for conjunctive keyword search in a single-writer single-reader framework. While the OXT protocol offers high performance by adopting a number of specialised data-structures, it also trades-off security by leaking 'partial' database information to the server. Recent attacks have exploited similar partial information leakage to breach database confidentiality. Consequently, it is an open problem to design SSE protocols that plug such leakages while retaining similar efficiency. In this paper, we propose a new SSE protocol, called Hidden Cross-Tags (HXT), that removes 'Keyword Pair Result Pattern' (KPRP) leakage for conjunctive keyword search. We avoid this leakage by adopting two additional cryptographic primitives - Hidden Vector Encryption (HVE) and probabilistic (Bloom filter) indexing into the HXT protocol. We propose a 'lightweight' HVE scheme that only uses efficient symmetric-key building blocks, and entirely avoids elliptic curve-based operations. At the same time, it affords selective simulation-security against an unbounded number of secret-key queries. Adopting this efficient HVE scheme, the overall practical storage and computational overheads of HXT over OXT are relatively small (no more than 10% for two keywords query, and 21% for six keywords query), while providing a higher level of security.

Network coding is a fundamental tool that enables higher network capacity and lower complexity in routing algorithms, by encouraging the mixing of information flows in the middle of a network. Implementing network coding in the core Internet is subject to practical concerns, since Internet routers are often overwhelmed by packet forwarding tasks, leaving little processing capacity for coding operations. Inspired by the recent paradigm of network function virtualization, we propose implementing network coding as a new network function, and deploying such coding functions in geo-distributed cloud data centers, to practically enable network coding on the Internet. We target multicast sessions (including unicast flows as special cases), strategically deploy relay nodes (network coding functions) in selected data centers between senders and receivers, and embrace high bandwidth efficiency brought by network coding with dynamic coding function deployment. We design and implement the network coding function on typical virtual machines, featuring efficient packet processing. We propose an efficient algorithm for coding function deployment, scaling in and out, in the presence of system dynamics. Real-world implementation on Amazon EC2 and Linode demonstrates significant throughput improvement and higher robustness of multicast via coding functions as well as efficiency of the dynamic deployment and scaling algorithm.