Sharing relational databases is essential in today’s data-driven world for fostering collaboration, enhancing efficiency, and enabling real-time data access. However, privacy and copyright issues arise when sharing privacy-sensitive or valuable data. Additionally, high utility is required in shared data to enable accurate data mining and analysis. Entry-level differentially private fingerprinting schemes (DPFS) could address these concerns. In a DPFS, data can be securely shared without leaking original values while still supporting accurate analysis. Moreover, detectable fingerprints can deter unauthorized redistribution. However, existing DPFSs often lack utility—due to format changes and entry-wise bias—or robustness, as fingerprints can be removed undetected. In this paper, we propose an unbiased and robust differential privacy-based fingerprinting scheme (DPFS), which ensures that the fingerprinted copy remains an unbiased estimate of the original data. By incorporating differential privacy noise, our scheme effectively mitigates alteration, collusion, and hybrid attacks. Our DPFS satisfies ϵ-entry-level differential privacy, enabling clients to conduct unbiased analysis. To improve robustness, we design group-based fingerprint detection, which estimates the mean of injected noise per group with error tolerance. We provide a theoretical robustness analysis and propose a method for achieving optimal robustness. Experiments on four real-world databases show that our scheme consistently detects fingerprints and improves accuracy by up to 20% on machine learning tasks compared to existing DPFSs.

Dynamic Searchable Symmetric Encryption (DSSE) plays a crucial role in secure cloud-based database systems, as it enables efficient keyword search and dynamic updates over encrypted data. However, practical deployment of DSSE schemes faces two significant challenges. First, clients may inadvertently perform faulty updates—such as re-adding an existing keyword-identifier pair or attempting to delete a non-existent one—which can compromise the correctness of subsequent search results. Second, even with correctly issued updates, malicious servers may return incorrect or incomplete search results, undermining data integrity. To address these challenges, we propose FVDSSE, the first fault-tolerant DSSE scheme that tolerates client-side operational faults and provides result verifiability against malicious servers. Moreover, it simultaneously ensures strong privacy by guaranteeing forward and backward privacy—two essential properties for any practical DSSE. To further optimize performance, we present FVDSSE-C, an enhanced variant that leverages caching techniques. Experimental evaluations on a real-world dataset show that FVDSSE-C achieves up to 130× improvement in search efficiency and 3× reduction in communication overhead compared to the state-of-the-art scheme (YCR22-C).

Detecting fraudulent online text is essential, as these manipulative messages exploit human greed, deceive individuals, and endanger societal security. Currently, this task remains under-explored on the Chinese web due to the lack of a comprehensive dataset of Chinese fraudulent texts. However, creating such a dataset is challenging because it requires extensive annotation within a vast collection of normal texts. Additionally, the creators of fraudulent webpages continuously update their tactics to evade detection by downstream platforms and promote fraudulent messages. To this end, this work firstly presents the comprehensive long-term dataset of Chinese fraudulent texts collected over 12 months, consisting of 59,106 entries extracted from billions of web pages. Furthermore, we design and provide a wide range of baselines, including large language model-based detectors, and pre-trained language model approaches. The necessary dataset and benchmark codes for further research are available via https://github.com/xuemingxxx/ChiFraud.

The increase in computing power and the necessity of AI-assisted decision-making boost the growing application of large language models (LLMs). Along with this, the potential retention of sensitive data of LLMs has spurred increasing research into machine unlearning. However, existing unlearning approaches face a critical dilemma: Aggressive unlearning compromises model utility, while conservative strategies preserve utility but risk hallucinated responses. This significantly limits LLMs' reliability in knowledge-intensive applications. To address this, we introduce a novel Attention-Shifting (AS) framework for selective unlearning. AS is driven by two design objectives: (1) context-preserving suppression that attenuates attention to fact-bearing tokens without disrupting LLMs' linguistic structure; and (2) hallucination-resistant response shaping that discourages fabricated completions when queried about unlearning content. AS realizes these objectives through two attention-level interventions, which are importance-aware suppression applied to the unlearning set to reduce reliance on memorized knowledge and attention-guided retention enhancement that reinforces attention toward semantically essential tokens in the retained dataset to mitigate unintended degradation. These two components are jointly optimized via a dual-loss objective, which forms a soft boundary that localizes unlearning while preserving unrelated knowledge under representation superposition. Experimental results show that AS improves performance preservation over the state-of-the-art unlearning methods, achieving up to 15\% higher accuracy on the ToFU benchmark and 10\% on the TDEC benchmark, while maintaining competitive hallucination-free unlearning effectiveness. Compared to existing methods, AS demonstrates a superior balance between unlearning effectiveness, generalization, and response reliability.

In light of increasing privacy concerns and stringent legal regulations, using secure multiparty computation (MPC) to enable collaborative GBDT model training among multiple data owners has garnered significant attention. Despite this, existing MPC-based GBDT frameworks face efficiency challenges due to high communication costs and the computation burden of non-linear operations, such as division and sigmoid calculations. In this work, we introduce Guard-GBDT, an innovative framework tailored for efficient and privacy-preserving GBDT training on vertical datasets. Guard-GBDT bypasses MPC-unfriendly division and sigmoid functions by using more streamlined approximations and reduces communication overhead by compressing the messages exchanged during gradient aggregation. We implement a prototype of Guard-GBDT and extensively evaluate its performance and accuracy on various real-world datasets. The results show that Guard-GBDT outperforms state-of-the-art HEP-XGB (CIKM’21) and SiGBDT (ASIA CCS’24) by up to $2.71 \times$ and $12.21 \times$ on LAN network and up to $2.7 \times$ and $8.2 \times$ on WAN network. Guard-GBDT also achieves comparable accuracy with SiGBDT and plaintext XGBoost (better than HEP-XGB), which exhibits a deviation of ±1% to ±2% only. Our implementation code is provided at https://github.com/XidianNSS/Guard-GBDT.git

The large language model (LLM) powered recommendation paradigm has been proposed to address the limitations of traditional recommender systems (RecSys), which often struggle to handle cold-start users or items with new IDs. Despite its effectiveness, this study uncovers that LLM-empowered RecSys are vulnerable to reconstruction attacks that can expose both system and user privacy. To thoroughly examine this threat, we present the first systematic study on inversion attacks targeting LLM-empowered RecSys, wherein adversaries attempt to reconstruct original prompts that contain personal preferences, interaction histories, and demographic attributes by exploiting the output logits of recommendation models. We reproduce the vec2text framework and optimize it using our proposed method - Similarity-Guided Refinement, enabling more accurate reconstruction of textual prompts from model-generated logits. Extensive experiments across two domains (movies and books) and two representative LLM-based recommendation models demonstrate that our method achieves high-fidelity reconstructions. Specifically, we can recover nearly 65% of the user-interacted items and correctly infer age and gender in 87% of the cases. The experiments also reveal that privacy leakage is largely insensitive to the victim model's performance but highly dependent on domain consistency and prompt complexity. These findings expose critical and privacy vulnerabilities in LLM-empowered RecSys. The code for reproduction is provided below: https://github.com/xuemingxxx/Attack_RecSys/

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.

Private learning for vertical decision trees (PVDT) is an emerging paradigm that allows multiple parties to execute cooperative training and inference of decision trees on vertically partitioned datasets, without revealing either party%'s data or model. The state-of-the-art PVDT schemes employ the secret-sharing-based secure multi-party computation (MPC) to admit low computational cost and low bandwidth. Nevertheless, existing schemes need many communication rounds for computing concrete protocols in PVDT, like the less-than comparison, division, etc. This property is not suited for large-communication-latency networks such as WAN. In this work, we present a two-party PVDT framework, calledSwan, to enable a secure, accurate, and fast realization of vertical decision trees. At the core of Swan, we design a secure and parallel protocol for$N$-input multiplication with one communication round. This forms the cornerstone for a series of secure and communication-efficient computation protocols specifically tailored to less-than comparison and division. Along the way, we use these optimized protocols to refine the training and inference processes of PVDT, achieving a significant reduction in both communication costs and rounds. Experimental results show Swan provides top-notch accuracy, and achieves a$10.2\times$and$2.8\times$improvement in online training and inference latency over WAN compared to prior art.

The decision tree (DT) model is widely usedin various applications due to its versatility, speed, and interpretability. However, outsourcing DT training and inference to cloud platforms raises data privacy concerns. While significant strides have been made in developing private DT training and inference using cryptography such as Secure Multi-Party Computation (MPC), the performance is still not ideal in real-world applications. Only a few recent works have explored using GPUs to enhance the performance of MPC-based deep learning. Nevertheless, data-dependent operations and the high communication costs inherent in MPC-based DT make the integration of GPUs a challenge. We introduce the eXpress GPU-based Tree (XGT), a fast MPC-based framework for private DT training and inference on GPUs.XGTconverts the majority of operations in training and inference into parallelizable matrix operations, supplemented by various optimizations, including matrix dimension reductions. This innovative design leads to substantial reductions in communication overhead while maintaining the critical property of obliviousness.XGTalso achieves a stronger security guarantee, where all data items, the tree shape, access patterns, and data distributions generated during the training and inference are protected.XGTonly reveals the tree depth. The experimental results show thatXGTis up to$278{\times }$faster than the previous most efficient CPU-based approach.XGToutperforms the latest GPU-based DT work by$41{\times }$. For inference,XGTis up to$2,800{\times }$faster than previous CPU-based inference schemes and at least$18 \times$faster than GPU-based.

Outsourcing Decision tree (DT) training and inference to cloud platforms raises privacy concerns. Recent Secure Multi-Party Computation (MPC)-based methods are hindered by heavy overhead. Few recent studies explored GPUs to improve MPC-protected deep learning, yet integrating GPUs into MPC-protected DT with massive data-dependent operations remains challenging, raising question: can MPC-protected DT training and inference fully leverage GPUs for optimal performance?We present GTree, the first scheme that exploits GPU to accelerate MPC-protected secure DT training and inference. GTree is built across 3 parties who jointly perform DT training and inference with GPUs. GTree is secure against semi-honest adversaries, ensuring that no sensitive information is disclosed. GTree offers enhanced security than prior solutions, which only reveal tree depth and data size while prior solutions also leak tree structure. With our oblivious array access, access patterns on GPU are also protected. To harness the full potential of GPUs, we design a novel tree encoding method and craft our MPC protocols into GPU-friendly versions. GTree achieves ~11× and ~21× improvements in training SPECT and Adult datasets, compared to prior most efficient CPU-based work. For inference, GTree outperforms the prior most efficient work by 126× when inferring 104instances with a 7-level tree.

A private decision tree evaluation (PDTE) protocol allows a feature vector owner (FO) to classify its data using a tree model from a model owner (MO) and only reveals an inference result to the FO. This paper proposes Mostree, a PDTE protocol secure in the presence of malicious parties with sublinear communication. We design Mostree in the three-party honest-majority setting, where an (untrusted) computing party (CP) assists the FO and MO in the secure computation. We propose two low-communication oblivious selection (OS) protocols by exploiting nice properties of three-party replicated secret sharing (RSS) and distributed point function. Mostree combines OS protocols with a tree encoding method and three-party secure computation to achieve sublinear communication. We observe that most of the protocol components already maintain privacy even in the presence of a malicious adversary, and what remains to achieve is correctness. To ensure correctness, we propose a set of lightweight consistency checks and seamlessly integrate them into Mostree. As a result, Mostree achieves sublinear communication and malicious security simultaneously. We implement Mostree and compare it with the state-of-the-art. Experimental results demonstrate that Mostree is efficient and comparable to semi-honest PDTE schemes with sublinear communication. For instance, when evaluated on the MNIST dataset in a LAN setting, Mostree achieves an evaluation using approximately 768 ms with communication of around 168 KB.

Outsourcing Machine Learning (ML) tasks to cloud servers is a cost-effective solution when dealing with distributed data. However, outsourcing these tasks to cloud servers could lead to data breaches. Secure computing methods, such as Homomorphic Encryption (HE) and Trusted Execution Environments (TEE), have been used to protect outsourced data. Nevertheless, HE remains inefficient in processing complicated functions (e.g., non-linear functions) and TEE (e.g., Intel SGX) is not ideal for directly processing ML tasks due to side-channel attacks and parallel-unfriendly computation. In this paper, we propose a hybrid framework integrating SGX and HE, called HT2ML, to protect user's data and models. In HT2ML, HE-friendly functions are protected with HE and performed outside the enclave, while the remaining operations are performed inside the enclave obliviously. HT2ML leverages optimised HE matrix multiplications to accelerate HE computations outside the enclave while using oblivious blocks inside the enclave to prevent access-pattern-based attacks. We evaluate HT2ML using Linear Regression (LR) training and Convolutional Neural Network (CNN) inference as two instantiations. The performance results show that HT2ML is up to ∼11× faster than HE only baseline with 6-dimensional data in LR training. For CNN inference, HT2ML is ∼196× faster than the most recent approach (Xiao et al., ICDCS'21).

Dynamic searchable symmetric encryption (DSSE) enables the data owner to outsource its database (document sets) to an untrusted server and make searches and updates securely and efficiently. Conjunctive DSSE can process conjunctive queries that return the documents containing multiple keywords. However, a conjunctive search could leak the keyword pair result pattern (KPRP), where attackers can learn which documents contain any two keywords involved in the query. File-injection attack shows that KPRP can be utilized to recover searched keywords. To protect data effectively, DSSE should also achieve forward privacy, i.e., hides the link between updates to previous searches, and backward privacy, i.e., prevents deleted entries being accessed by subsequent searches. Otherwise, the attacker could recover updated/searched keywords and records. However, no conjunctive DSSE scheme in the literature can hide KPRP in sub-linear search efficiency while guaranteeing forward and backward privacy. In this work, we propose the first sub-linear KPRP-hiding conjunctive DSSE scheme (named HDXT) with both forward and backward privacy guarantees. To achieve these three security properties, we introduce a new cryptographic primitive: Attribute-updatable Hidden Map Encryption (AUHME). AUHME enables HDXT to efficiently and securely perform conjunctive queries and update the database in an oblivious way. In comparison with previous work that has weaker security guarantees, HDXT shows comparable, and in some cases, even better performance.

Private decision tree evaluation (PDTE) allows a decision tree holder to run a secure protocol with a feature provider. By running the protocol, the feature provider will learn a classification result. Nothing more is revealed to either party. In most existing PDTE protocols, the required communication grows exponentially with the tree's depth d, which is highly inefficient for large trees. This shortcoming motivated us to design a sublinear PDTE protocol with $O(d)$ communication complexity. The core of our construction is a shared oblivious selection (SOS) functionality, allowing two parties to perform a secret-shared oblivious read operation from an array. We provide two SOS protocols, both of which achieve sublinear communication and propose optimizations to further improve their efficiency. Our sublinear PDTE protocol is based on the proposed SOS functionality and we prove its security under a semi-honest adversary. We compare our protocol with the state-of-the-art, in terms of communication and computation, under various network settings. The performance evaluation shows that our protocol is practical and more scalable over large trees than existing solutions.

The classification service over a stream of data is becoming an important offering for cloud providers, but users may encounter obstacles in providing sensitive data due to privacy concerns. While Trusted Execution Environments (TEEs) are promising solutions for protecting private data, they remain vulnerable to side-channel attacks induced by data-dependent access patterns. We propose a Privacy-preserving Data Stream Training and Inference scheme, called EnclaveTree, that provides confidentiality for user's data and the target models against a compromised cloud service provider. We design a matrix-based training and inference procedure to train the Hoeffding Tree (HT) model and perform inference with the trained model inside the trusted area of TEEs, which provably prevent the exploitation of access-pattern-based attacks. The performance evaluation shows that EnclaveTree is practical for processing the data streams with small or medium number of features. When there are less than 63 binary features,EnclaveTree is up to ~10x and ~9 faster than naïve oblivious solution on training and inference, respectively.

Verifiable Dynamic Searchable Symmetric Encryption (VDSSE) enables users to securely outsource databases (document sets) to cloud servers and perform searches and updates. The verifiability property prevents users from accepting incorrect search results returned by a malicious server. However, we discover that the community currently only focuses on preventing malicious behavior from the server but ignores incorrect updates from the client, which are very likely to happen since there is no record on the client to check. Indeed most existing VDSSE schemes are not sufficient to tolerate incorrect updates from the client. For instance, deleting a nonexistent keyword-identifier pair can break their correctness and soundness. In this paper, we demonstrate the vulnerabilities of a type of existing VDSSE schemes that fail them to ensure correctness and soundness properties on incorrect updates. We propose an efficient fault-tolerant solution that can consider any DSSE scheme as a black-box and make them into a fault-tolerant VDSSE in the malicious model. Forward privacy is an important property of DSSE that prevents the server from linking an update operation to previous search queries. Our approach can also make any forward secure DSSE scheme into a fault-tolerant VDSSE without breaking the forward security guarantee. In this work, we take FAST [1] (TDSC 2020), a forward secure DSSE, as an example, implement a prototype of our solution, and evaluate its performance. Even when compared with the previous fastest forward private construction that does not support fault tolerance, the experiments show that our construction saves 9× client storage and has better search and update efficiency.

The Publish and Subscribe (pub/sub) system is an established paradigm to disseminate the data from publishers to subscribers in a loosely coupled manner using a network of dedicated brokers. However, sensitive data could be exposed to malicious entities if brokers get compromised or hacked; or even worse, if brokers themselves are curious to learn about the data. A viable mechanism to protect sensitive publications and subscriptions is to encrypt the data before it is disseminated through the brokers. State-of-the-art approaches allow brokers to perform encrypted matching without revealing publications and subscriptions. However, if malicious brokers collude with malicious subscribers or publishers, they can learn the interests of innocent subscribers, even when the interests are encrypted. In this article, we present a pub/sub system that ensures confidentiality of publications and subscriptions in the presence of untrusted brokers. Furthermore, our solution resists collusion attacks between untrusted brokers and malicious subscribers (or publishers). Finally, we have implemented a prototype of our solution to show its feasibility and efficiency.

Searchable Encryption (SE) is a technique that allows Cloud Service Providers to search over encrypted datasets without learning the content of queries and records. In recent years, many SE schemes have been proposed to protect outsourced data. However, most of them leak sensitive information, from which attackers could still infer the content of queries and records by mounting leakage-based inference attacks, such as the count attack and file-injection attack . In this work, first we define the leakage in searchable encrypted databases and analyse how the leakage is leveraged in existing leakage-based attacks. Second, we propose a Privacy-preserving Multi-cloud based dynamic symmetric SE scheme for relational Database ( P-McDb ). P-McDb has minimal leakage, which not only ensures confidentiality of queries and records but also protects the search, intersection, and size patterns. Moreover, P-McDb ensures both forward and backward privacy of the database. Thus, P-McDb could resist existing leakage-based attacks, e.g., active file/record-injection attacks. We give security definition and analysis to show how P-McDb hides the aforementioned patterns. Finally, we implemented a prototype of P-McDb and tested it using the TPC-H benchmark dataset. Our evaluation results show that users can get the required records in 2.16 s when searching over 4.1 million records.

Large-scale data training is vital to the generalization performance of deep learning (DL) models. However, collecting data directly is associated with increased risk of privacy disclosure, particularly in special fields such as healthcare, finance, and genomics. To protect training data privacy, collaborative deep learning (CDL) has been proposed to enable joint training from multiple data owners while providing reliable privacy guarantee. However, recent studies have shown that CDL is vulnerable to several attacks that could reveal sensitive information about the original training data. One of the most powerful attacks benefits from the leakage from gradient sharing during collaborative training process. In this study, we present a new CDL framework, PrivateDL, to effectively protect private training data against leakage from gradient sharing. Unlike conventional training process that trains on private data directly, PrivateDL allows effective transfer of relational knowledge from sensitive data to public data in a privacy-preserving way, and enables participants to jointly learn local models based on the public data with noise-preserving labels. This way, PrivateDL establishes a privacy gap between the local models and the private datasets, thereby ensuring privacy against the attacks launched to the local models through gradient sharing. Moreover, we propose a new algorithm called Distributed Aggregation Stochastic Gradient Descent, which is designed to improve the efficiency and accuracy of CDL, especially in the asynchronous training mode. Experimental results demonstrate that PrivateDL preserves data privacy with reasonable performance overhead.

A Content Delivery Network (CDN) is a distributed system composed of a large number of nodes that allows users to request objects from nearby nodes. CDN not only reduces end-to-end latency on the user side but also offloads Content Providers (CPs), providing resilience against Distributed Denial of Service (DDoS) attacks. However, by caching objects and processing user requests, CDN providers could infer user preferences and the popularity of objects, thus resulting in information leakage. Unfortunately, such information leakage may result in loss of user privacy and reveal business-specific information to untrusted or compromised CDN providers. State-of-the-art solutions can protect the content of sensitive objects but cannot prevent CDN providers from inferring user preferences and the popularity of objects. In this work, we present a privacy-preserving encrypted CDN system to hide not only the content of objects and user requests, but also protect user preferences and the popularity of objects from curious CDN providers. We employ encryption to protect the objects and user requests in a way that both the CDNs and CPs can perform the search operations without accessing objects and requests in cleartext. Our proposed system is based on a scalable key management approach for multi-user access, where no key regeneration and data re-encryption are needed for user revocation. We have implemented a prototype of the system and show its practical efficiency.

In blockchain-based systems, malicious behaviour can be detected using auditable information in transactions managed by distributed ledgers. Besides cryptocurrency, blockchain technology has recently been used for other applications, such as file storage. However, most of existing blockchain- based file storage systems can not revoke a user efficiently when multiple users have access to the same file that is encrypted. Actually, they need to update file encryption keys and distribute new keys to remaining users, which significantly increases computation and bandwidth overheads. In this work, we propose a blockchain and proxy re-encryption based design for encrypted file sharing that brings a distributed access control and data management. By combining blockchain with proxy re-encryption, our approach not only ensures confidentiality and integrity of files, but also provides a scalable key management mechanism for file sharing among multiple users. Moreover, by storing encrypted files and related keys in a distributed way, our method can resist collusion attacks between revoked users and distributed proxies.

Outsourcing sensitive data and operations to untrusted cloud providers is considered a challenging issue. To perform a search operation, even if both the data and the query are encrypted, attackers still can learn which data locations match the query and what results are returned to the user. This kind of leakage is referred to as data access pattern. Indeed, using access pattern leakage, attackers can easily infer the content of the data and the query. Oblivious RAM (ORAM), Fully Homomorphic Encryption (FHE), and secure Multi- Party Computation (MPC) offer a higher level of security but incur high computation and communication overheads. One promising practical approach to process the outsourced data efficiently and securely is leveraging trusted hardware like Intel SGX. Recently, several SGX- based solutions have been proposed in the literature. However, those solutions suffer from side channel attacks, high overheads of context switching, or limited SGX memory. In this paper, we present an SGX-assisted scheme for performing search over encrypted data. Our solution protects access pattern against side channel attacks while ensuring search efficiency. It can process large databases without requiring any long-term storage on SGX. We have implemented a prototype of the scheme and evaluated its performance using a dataset of 1 million records. The equality query and range query can be completed in 11 and 40 milliseconds, respectively. Comparing with ORAM- based solutions, such as ObliDB, our scheme is more than 10x faster.

Searchable Symmetric Encryption (SSE) allows users to execute encrypted queries over encrypted databases. A large number of SSE schemes have been proposed in the literature. However, most of them leak a significant amount of information that could lead to inference attacks. In this work, we propose an SSE scheme for a Privacy-preserving Multi-cloud encrypted Database (P-McDb), which aims at preventing inference attacks. P-McDb allows users to execute SQL-like queries in an efficient sub-linear manner without leaking search, access and size patterns. We have implemented a prototype of P-McDb and show its practical efficiency.

A Content Delivery Network (CDN) is a distributed system composed of a large number of nodes that allows users to request objects from nearby nodes. CDN not only reduces the end-to-end latency on the user side but also offloads Content Providers (CPs) providing resilience against Distributed Denial of Service (DDoS) attacks. However, by caching objects and processing users' requests, CDN service providers could infer user preferences and the popularity of objects, thus resulting in information leakage. Unfortunately, such information leakage may result in compromising users' privacy and reveal business-specific information to untrusted or potentially malicious CDN providers. State-of-the-art Searchable Encryption (SE) schemescan protect the content of sensitive objects but cannot preventthe CDN providers from inferring users' preferences and thepopularity of objects. In this work, we present a privacy-preserving encrypted CDN system not only to hide the content of objects and users' requests, but also to protect users' preferences and the popularity of objects from curious CDN providers. We encrypt the objects and user requests in a way that both the CDNs and CPs can perform the search operations without accessing those objects and requests in cleartext. Our proposed system is based on a scalable key management approach for multi-user access, where no key regeneration and data re-encryption are needed for user revocation.