Yuepeng Hu, Zhengyuan Jiang, Mengyuan Li, Osama Ahmed, Zhicong Huang, Cheng Hong, Neil Zhenqiang Gong. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Many machine learning models are fine-tuned from large language models (LLMs) to achieve high performance in specialized domains like code generation, biomedical analysis, and mathematical problem solving. However, this fine-tuning process often introduces a critical vulnerability: the systematic degradation of safety alignment, undermining ethical guidelines and increasing the risk of harmful outputs. Addressing this challenge, we introduce EnchTable, a novel framework designed to transfer and maintain safety alignment in downstream LLMs without requiring extensive retraining. EnchTable leverages a Neural Tangent Kernel (NTK)-based safety vector distillation method to decouple safety constraints from task-specific reasoning, ensuring compatibility across diverse model architectures and sizes. Additionally, our interference-aware merging technique effectively balances safety and utility, minimizing performance compromises across various task domains. We implemented a fully functional prototype of EnchTable on three different task domains and three distinct LLM architectures, and evaluated its performance through extensive experiments on eleven diverse datasets, assessing both utility and model safety. Our evaluations include LLMs from different vendors, demonstrating EnchTable's generalization capability. Furthermore, EnchTable exhibits robust resistance to static and dynamic jailbreaking attacks, outperforming vendor-released safety models in mitigating adversarial prompts. Comparative analyses with six parameter modification methods and two inference-time alignment baselines reveal that EnchTable achieves a significantly lower unsafe rate, higher utility score, and universal applicability across different task domains. Additionally, we validate EnchTable can be seamlessly integrated into various deployment pipelines without significant overhead.

ChatGPT is recognized as a significant revolution in the field of artificial intelligence, but it raises serious concerns regarding user privacy, as the data submitted by users may contain sensitive information. Existing solutions for secure inference face significant challenges in supporting GPT-like models due to the enormous number of model parameters and complex activation functions. In this paper, we develop CipherGPT, the first framework for secure two-party GPT inference, building upon a series of innovative protocols. First, we propose a secure matrix multiplication that is customized for GPT inference, achieving upto 3.8× speedup and 4.3× bandwidth reduction over SOTA. We also propose a novel protocol for securely computing GELU, surpassing SOTA by 3.2× in runtime, 1.3× in communication and 7.4× in precision. Furthermore, we propose the first protocol for secure top-k sampling. We provide a full-fledged implementation and comprehensive benchmark for CipherGPT. In particular, we measure the runtime and communication for each individual operation. We believe this will serve as a reference for future research in this area.

In the era of Big Data, enterprises and individuals often upload databases to the cloud for storage and querying, which involves the risk of data leakage. Encrypted databases based on fully homomorphic encryption (FHE) theoretically solve the leakage problem, but the actual deployment of such encrypted databases faces the challenge of high economic costs. Cloud service providers charge for data transfer volume and computation time. Unfortunately, FHE is very expensive in both aspects, with more than five orders of magnitude deterioration compared to directly transmitting and computing plaintext. In this paper, we present STEED, a low-cost encrypted database that tackles both bottlenecks simultaneously. In STEED, we first introduce a FHE framework called BatchPBS, a batch pro grammable bootstrapping framework that improves the recent Liu and Wang (ASIACRYPT 2023) amortised scheme from 6.7 ms to 3 msper ciphertext while adding multi-value bootstrapping (MVB) support. Based on BatchPBS, we propose efficient SQL algorithms in SIMD-style to reduce the computation time and a novel AES transcipher protocol to reduce the data transfer volume. Thus, STEED reduces query time by 13 × and data transfer amount by 165 to 534.9 × compared with SOTA work. Considering end-to-end economic cost of TPC-H query on a database with 1 million rows, STEED reduces the expense of deploying on AWS by $28444.8 per 100 queries. (The code can be found at https://github.com/alibaba-damo-academy/ctl-he)

Secure two-party neural network (2PC-NN) inference is a privacy-preserving inference method that protects the client's input and the server's model parameters. While addressing privacy concerns, it also incurs considerable over-heads. In this work, we propose Octopus, a faster and more communication-efficient 2PC-NN system than prior works. Octopus designs an optimized encoding method for fast homomorphic convolution, and further constructs homomorphic encryption-based convolutional computation protocol. Compared with the original coefficient encoding proposed by Cheetah, our method significantly reduces the resulting ciphertexts through packing output channels, thereby saving the communication cost and end-to-end execution time. Moreover, Octopus proposes an encoding-motivated fine tuning technique for convolutional neural networks, which fully utilizes the feature of coefficient encoding to adaptively adjust the neural network structure to maximize performance with negligible accuracy loss. We apply Octopus to the widely used model ResNet on CIFAR-10 and ImageNet dataset. Experiments illustrate that Octopus has obvious improvement compared with the state-of-the-art approaches, achieving a speedup of up to 2.75×, and reduces communication overhead by up to 7.19× for convolutions. As for secure inference, compared with Cheetah (resp., CrypTFlow2), Octopus demonstrates 1.41× (resp., 13.20×) lower communication cost and 1.25× (resp., 7.03×) faster execution time under a WAN setting.

Approximate nearest neighbor search (ANNS), also known as vector search, is an important building block for various applications, such as recommendation systems, biometric authentication, and machine learning. In this work, we are interested in the private ANNS problem, where the client wants to learn (and can only learn) the ANNS results without revealing the query to the server. Previous private ANNS works either suffer from high communication cost (Chen et al., USENIX Security 2020) or work under a stronger security assumption of two non-colluding servers (Servan-Schreiber et al., SP 2022). We present Panther, an efficient private ANNS framework under the single server setting. Panther achieves its high performance via several novel co-designs of private information retrieval, secret-sharing, garbled circuits, and homomorphic encryption. We made extensive experiments using Panther on four public datasets, showing that Panther could answer an ANNS query on 10 million points in 18 seconds with 284 MB of communication. This is more than 7.8× faster and 20× more compact than Chen et al.

Federated Learning (FL) is a distributed paradigm aimed at protecting participant data privacy by exchanging model parameters to achieve high-quality model training. However, this distributed nature also makes FL highly vulnerable to backdoor attacks. Notably, the recently proposed state-of-the-art (SOTA) attack, 3DFed (SP2023), uses an indicator mechanism to determine whether the backdoor models have been accepted by the defender and adaptively optimizes backdoor models, rendering existing defenses ineffective. In this paper, we first reveal that the failure of existing defenses lies in the employment of empirical statistical measures that are loosely coupled with backdoor attacks. Motivated by this, we propose a Malignity-Aware backdooR defenSe (MARS) that leverages backdoor energy (BE) to indicate the malicious extent of each neuron. To amplify malignity, we further extract the most prominent BE values from each model to form a concentrated backdoor energy (CBE). Finally, a novel Wasserstein distance-based clustering method is introduced to effectively identify backdoor models. Extensive experiments demonstrate that MARS can defend against SOTA backdoor attacks and significantly outperforms existing defenses.

Recent years have seen great improvements in zero-knowledge proofs (ZKPs). Among them, zero-knowledge SNARKs are notable for their compact and efficiently-verifiable proofs, but suffer from high prover costs. Wu et al. (Usenix Security 2018) proposed to distribute the proving task across multiple machines, and achieved significant improvements in proving time. However, existing distributed ZKP systems still have quasi-linear prover cost, and may incur a communication cost that is linear in circuit size. In this paper, we introduce HyperPianist. Inspired by the state-of-the-art distributed ZKP system Pianist (Liu et al., S&P 2024) and the multivariate proof system HyperPlonk (Chen et al., EUROCRYPT 2023), we design a distributed multivariate polynomial interactive oracle proof (PIOP) system with a linear-time prover cost and logarithmic communication cost. Unlike Pianist, HyperPianist incurs no extra overhead in prover time or communication when applied to general (non-data-parallel) circuits. To instantiate the PIOP system, we adapt two additively-homomorphic multivariate polynomial commitment schemes, multivariate KZG (Papamanthou et al., TCC 2013) and Dory (Lee et al., TCC 2021), into the distributed setting, and get HyperPianistKand HyperPianistDrespectively. Both systems have linear prover complexity and logarithmic communication cost; furthermore, HyperPianistDrequires no trusted setup. We also propose HyperPianist+, incorporating an optimized lookup argument based on Lasso (Setty et al., EUROCRYPT 2024) with lower prover cost. Experiments demonstrate HyperPianistKand HyperPianistDachieve speedups of 63.1x and 40.2x over HyperPlonk with 32 distributed machines. Compared to Pianist, HyperPianistKcan be 2.9x and 4.6x as fast and HyperPianistDcan be 2.4x and 3.8x as fast, on vanilla gates and custom gates respectively. With layered circuits, HyperPianistKis up to 5.9x as fast on custom gates, and HyperPianistDachieves a 4.7x speedup.

Homomorphic Encryption (HE) allows computations on encrypted data without decryption. It can be used where the users' information are to be processed by an untrustful server, and has been a popular choice in privacy-preserving applications. However, in order to obtain meaningful results, we have to assume an honest-but-curious server, i.e., it will faithfully follow what was asked to do. If the server is malicious, there is no guarantee that the computed result is correct. The notion of verifiable HE (vHE) is introduced to detect malicious server's behaviors, but current vHE schemes are either more than four orders of magnitude slower than the underlying HE operations (Atapoor et. al, CIC 2024) or fast but incompatible with server-side private inputs (Chatel et. al, CCS 2024). In this work, we propose a vHE framework ZHE: efficient Zero-Knowledge Proofs (ZKPs) that prove the correct execution of HE evaluations while protecting the server's private inputs. More precisely, we first design two new highly-efficient ZKPs for modulo operations and (Inverse) Number Theoretic Transforms (NTTs), two of the basic operations of HE evaluations. Then we build a customized ZKP for HE evaluations, which is scalable, enjoys a fast prover time and has a non-interactive online phase. Our ZKP is applicable to all Ring-LWE based HE schemes, such as BGV and CKKS. Finally, we implement our protocols for both BGV and CKKS and conduct extensive experiments on various HE workloads. Compared to the state-of-the-art works, both of our prover time and verifier time are improved; especially, our prover cost is only roughly 27–36× more expensive than the underlying HE operations, this is two to three orders of magnitude cheaper than state-of-the-arts.

Privacy preservation has become a critical concern for governments, hospitals, and large corporations. Homomorphic encryption (HE) enables a ciphertext-based computation paradigm with strong security guarantees. In emerging cross-agency data cooperation scenarios like vertical federated learning (VFL), HE protects the data interaction from exposure to counterparts. However, computation on ciphertext has significant performance challenges due to increased data size and substantial overhead. Related work has been proposed to accelerate HE using parallel hardware, such as GPUs, FPGAs, and ASICs. However, many existing hardware accelerators target specific HE operations, such as number theoretic transform (NTT) and key switching, providing limited performance improvement for end-to-end applications. Others support bootstrapping, which requires quite a large ASIC design. To better support existing VFL training applications, we propose SAFE, an HE accelerator for scalable homomorphic matrix-vector products (HMVPs), which is the performance bottleneck. SAFE adopts a coefficient-wise encoded HMVP algorithm, despite a vanilla mode, we further explore the compressed and concatenated modes, which can fully utilize the polynomial encoding slots. The proposed hardware architecture, customized for HMVP dataflow, supports spatial and temporal parallelization of function units. The most costly polynomial function, NTT, is implemented with a low-area constant geometry unit which improves efficiency by$2.43\times $. SAFE is implemented as a CPU-FPGA heterogeneous acceleration system, unleashing the multithread potential. The evaluation demonstrates an up to$36\times $speed-up in end-to-end federated logistic regression training.

Reverse multiplication-friendly embeddings have played a crucial role in secure multiparty computation and zero-knowledge proofs. In this work, we generalize the notion of RMFEs todegree-DRMFEs. We present a general construction of degree-DRMFEs by generalizing the ideas on algebraic geometry used to construct traditional degree-2 RMFEs. Furthermore, our theory is given in a unified manner for general Galois rings, which include both rings of the form Zpkand fields like Fpk, which have been treated separately in prior works. We present multiple concrete sets of parameters for degree-DRMFEs (includingD= 2), which can be useful for future works. In the recent work of (Cheon & Lee, Eurocrypt’22), the concept of adegree-D packing methodwas formally introduced, which captures the idea of embedding multiple elements of a smaller ring into a larger ring. We show that the generalized notion of RMFEs todegree-D RMFEswhich, in spite of being “more algebraic” than packing methods, turn out to be essentially equivalent. Thus, our constructions of degree-DRMFEs are also degree-Dpacking methods.

Achieving malicious security with high efficiency in dishonest-majority secure multiparty computation is a formidable challenge. The milestone works SPDZ and TinyOT have spawn a large family of protocols in this direction. For boolean circuits, state-of-the-art works (Cascudo et. al, TCC 2020 and Escudero et. al, CRYPTO 2022) have proposed schemes based on reverse multiplication-friendly embedding (RMFE) to reduce the amortized cost. However, these protocols are theoretically described and analyzed, resulting in a significant gap between theory and concrete efficiency.

Multiple works have designed or used maliciously secure honest majority MPC protocols over Z2k using replicated secret sharing (e.g. Koti et al. USENIX'21). A recent trend in the design of such MPC protocols is to first execute a semi-honest protocol, and then use a check that verifies the correctness of the computation requiring only sublinear amount of communication in terms of the circuit size. The so-called Galois ring extensions are needed in order to execute such checks over Z2k, but these rings incur incredibly high computation overheads, which completely undermine any potential benefits the ring Z2k had to begin with.

Homomorphic encryption (HE) is a promising technique for privacy-preserving computing because it allows computation on encrypted data without decryption. HE, however, suffers from poor performance due to enlarged data size and exploded amount of computation. Related work has been proposed to accelerate HE using GPUs, FPGAs, and ASICs. The existing work, however, aims at specific HE schemes and fails to consider the fast-evolving algorithms. For example, HE algorithms that combine different HE schemes have demonstrated capability of supporting more types of HE operations and ciphertexts. Moreover, some existing hardware accelerators target small HE operations (such as number theoretic transform and key-switch), which however provides limited or even neglected performance improvement for end-to-end applications. To better support existing privacy-preserving applications (e.g., logistic regression and neural network inference), we propose CHAM, an HE accelerator, for high-performance matrix-vector product, which can be easily extended to 2-D and 3-D convolutions. Motivated by the evolution of algorithms, CHAM supports not only traditional HE operations, but also different types of ciphertexts and the conversion between them. We implement CHAM with Xilinx FPGAs. The evaluation demonstrates 1800× speed-up for matrix-vector product, 36× speed-up for logistic regression, and 144× speed-up for Beaver triple generation compared to the existing work.

Secret Sharing (SS) is widely adopted in secure Multi-Party Computation (MPC) with its simplicity and computational efficiency. However, SS-based MPC protocol introduces significant communication overhead due to interactive operations on secret sharings over the network. For instance, training a neural network model with SS-based MPC may incur tens of thousands of communication rounds among parties, making it extremely hard for real-world deployment.To reduce the communication overhead of SS, prior works statically convert interactive operations to equivalent non-interactive operations with extra computation cost. However, we show that such static conversion misses chances for optimization, and further present SOLAR, an SS-based MPC framework that aims to reduce the communication overhead through dynamic communication-computation conversion. At its heart, SOLAR converts interactive operations that involve communication among parties to equivalent non-interactive operations within each party with extra computations and introduces a speculative strategy to perform opportunistic conversion when CPU is idle for network transmission. We have implemented and evaluated SOLAR on several popular MPC applications, and achieved 1.6-8.1 times speedup in multi-thread setting compared to the basic SS and 1.2-8.6 times speedup over static conversion.

With recent advances in homomorphic encryption (HE), it becomes feasible to run non-interactive machine learning (ML) algorithms on encrypted data without decryption. In this work, we propose novel encoding methods to pack matrix in a compact way and more efficient methods to perform matrix multiplication on homomorphically encrypted data, leading to a speed boost of$1.5\times - 20\times$for slim rectangular matrix multiplication compared with state-of-the-art. Moreover, we integrate our optimized secure matrix arithmetic with the MPI distributed computing framework, achieving scalable parallel secure matrix computation. Equipped with the optimized matrix multiplication, we proposeuSCORE, a privacy-preserving cross-domain recommendation system for the unbalanced scenario, where a big data owner provides recommendation as a service to a client who has less data and computation power. Our design delegates most of the computation to the service provider, and has a low communication cost, which previous works failed to achieve. For a client who has 16 million user-item pairs to update, it only needs about 3 minutes (in the LAN setting) to prepare the encrypted data. The server can finish the update process on the encrypted data in less than half an hour, effectively reducing the client's test error from 0.72 to 0.62.

Logistic Regression (LR) is the most widely used machine learning model in industry for its efficiency, robustness, and interpretability. Due to the problem of data isolation and the requirement of high model performance, many applications in industry call for building a secure and efficient LR model for multiple parties. Most existing work uses either Homomorphic Encryption (HE) or Secret Sharing (SS) to build secure LR. HE based methods can deal with high-dimensional sparse features, but they incur potential security risks. SS based methods have provable security, but they have efficiency issue under high-dimensional sparse features. In this paper, we first present CAESAR, which combines HE and SS to build secure large-scale sparse logistic regression model and achieves both efficiency and security. We then present the distributed implementation of CAESAR for scalability requirement. We have deployed CAESAR in a risk control task and conducted comprehensive experiments. Our experimental results show that CAESAR improves the state-of-the-art model by around 130 times.

Homomorphic encryption (HE) is considered as one of the most important primitives for privacy-preserving applications. However, an efficient approach to evaluate both polynomial and non-polynomial functions on encrypted data is still absent, which hinders the deployment of HE to real-life applications. To address this issue, we propose a practical framework PEGASUS. PEGASUS can efficiently switch back and forth between a packed CKKS ciphertext and FHEW ciphertexts without decryption, allowing us to evaluate arithmetic functions efficiently on the CKKS side, and to evaluate look-up tables on FHEW ciphertexts. Our FHEW → CKKS conversion algorithm is more practical than the existing methods. We improve the computational complexity from linear to sublinear. Moreover, the size of our conversion key is significantly smaller, e.g., reduced from 80 gigabytes to 12 megabytes. We present extensive benchmarks of PEGASUS, including sigmoid/ReLU/min/max/division, sorting and max-pooling. To further demonstrate the capability of PEGASUS, we developed two more applications. The first one is a private decision tree evaluation whose communication cost is about two orders of magnitude smaller than the previous HE-based approaches. The second one is a secure K-means clustering that is able to run on thousands of encrypted samples in minutes that outperforms the best existing system by 14 × – 20×. To the best of our knowledge, this is the first work that supports practical K-means clustering using HE in a single server setting.

Nowadays, privacy preserving machine learning has been drawing much attention in both industry and academy. Meanwhile, recommender systems have been extensively adopted by many commercial platforms (e.g. Amazon) and they are mainly built based on user-item interactions. Besides, social platforms (e.g. Facebook) have rich resources of user social information. It is well known that social information, which is rich on social platforms such as Facebook, are useful to build intelligent recommender systems. It is anticipated to combine the social information with the user-item ratings to improve the overall recommendation performance. Most existing recommendation models are built based on the assumptions that the social information are available. However, different platforms are usually reluctant to (or can not) share their data due to certain concerns. In this paper, we first propose a SEcure SOcial RECommendation (SeSoRec) framework which is able to (1) collaboratively mine knowledge from social platform to improve the recommendation performance of the rating platform, and (2) securely keep the raw data of both platforms. We then propose a Secret Sharing based Matrix Multiplication (SSMM) protocol to optimize SeSoRec and prove its correctness and security theoretically. By applying minibatch gradient descent, SeSoRec has linear time complexities in terms of both computation and communication. The comprehensive experimental results on three real-world datasets demonstrate the effectiveness of our proposed SeSoRec and SSMM.

Homomorphic Encryption (HE) allows encrypted data to be processed without decryption, which could maximize the protection of user privacy without affecting the data utility. Thanks to strides made by cryptographers in the past few years, the efficiency of HE has been drastically improved, and machine learning on homomorphically encrypted data has become possible. Several works have explored machine learning based on HE, but most of them are restricted to the outsourced scenario, where all the data comes from a single data owner. We propose HomoPAI, an HE-based secure collaborative machine learning system, enabling a more promising scenario, where data from multiple data owners could be securely processed. Moreover, we integrate our system with the popular MPI framework to achieve parallel HE computations. Experiments show that our system can train a logistic regression model on millions of homomorphically encrypted data in less than two minutes.

Homomorphic Encryption (HE) based secure Neural Networks(NNs) inference is one of the most promising security solutions to emerging Machine Learning as a Service (MLaaS). In the HE-based MLaaS setting, a client encrypts the sensitive data, and uploads the encrypted data to the server that directly processes the encrypted data without decryption, and returns the encrypted result to the client. The clients' data privacy is preserved since only the client has the private key. Existing HE-enabled Neural Networks (HENNs), however, suffer from heavy computational overheads. The state-of-the-art HENNs adopt ciphertext packing techniques to reduce homomorphic multiplications by packing multiple messages into one single ciphertext. Nevertheless, rotations are required in these HENNs to implement the sum of the elements within the same ciphertext. We observed that HENNs have to pay significant computing overhead on rotations, and each of rotations is $\sim 10\times$ more expensive than homomorphic multiplications between ciphertext and plaintext. So the massive rotations have become a primary obstacle of efficient HENNs. In this paper, we propose a fast, frequency-domain deep neural network called Falcon, for fast inferences on encrypted data. Falcon includes a fast Homomorphic Discrete Fourier Transform (HDFT) using block-circulant matrices to homomorphically support spectral operations. We also propose several efficient methods to reduce inference latency, including Homomorphic Spectral Convolution and Homomorphic Spectral Fully Connected operations by combing the batched HE and block-circulant matrices. Our experimental results show Falcon achieves the state-of-the-art inference accuracy and reduces the inference latency by $45.45\%\sim 85.34\%$ over prior HENNs on MNIST and CIFAR-10.

When collecting information, local differential privacy (LDP) alleviates privacy concerns of users because their private information is randomized before being sent it to the central aggregator. LDP imposes large amount of noise as each user executes the randomization independently. To address this issue, recent work introduced an intermediate server with the assumption that this intermediate server does not collude with the aggregator. Under this assumption, less noise can be added to achieve the same privacy guarantee as LDP, thus improving utility for the data collection task. This paper investigates this multiple-party setting of LDP. We analyze the system model and identify potential adversaries. We then make two improvements: a new algorithm that achieves a better privacy-utility tradeoff; and a novel protocol that provides better protection against various attacks. Finally, we perform experiments to compare different methods and demonstrate the benefits of using our proposed method.

Multi-dimensional analytical (MDA) queries are often issued against a fact table with predicates on (categorical or ordinal) dimensions and aggregations on one or more measures. In this paper, we study the problem of answering MDA queries under local differential privacy (LDP). In the absence of a trusted agent, sensitive dimensions are encoded in a privacy-preserving (LDP) way locally before being sent to the data collector. The data collector estimates the answers to MDA queries, based on the encoded dimensions. We propose several LDP encoders and estimation algorithms, to handle a large class of MDA queries with different types of predicates and aggregation functions. Our techniques are able to answer these queries with tight error bounds and scale well in high-dimensional settings (i.e., error is polylogarithmic in dimension sizes). We conduct experiments on real and synthetic data to verify our theoretical results, and compare our solution with marginal-estimation based solutions.

Differential privacy has emerged as the de facto standard for privacy definitions, and been used by, e.g ., Apple, Google, Uber, and Microsoft, to collect sensitive information about users and to build privacy-preserving analytics engines. However, most of such advanced privacy-protection techniques are not accessible to mid-size companies and app developers in the cloud. We demonstrate a lightweight middleware DPSAaS , which provides d ifferentially p rivate data-sharing-and-analytics functionality a s cloud services. We focus on multi-dimensional analytical (MDA) queries under local differential privacy (LDP) in this demo. MDA queries against a fact table have predicates on (categorical or ordinal) dimensions and aggregate one or more measures. In the absence of a trusted agent, sensitive dimensions and measures are encoded in a privacy-preserving way locally using our LDP data sharing service, before being sent to the data collector. The data collector estimates the answers to MDA queries from the encoded data, using our data analytics service. We will highlight the design decisions of DPSAaS and twists made to LDA algorithms to fit the design, in order to smoothly connect DPSAaS to the data processing platform and analytics engines, and to facilitate efficient large-scale processing.

There exists a specific security issue in symmetric searchable encryption that, when doing CKS(Conjunctive Keywords Search), the trapdoors and search results may reveal the relationships between the keywords being searched. For example, if the search result of keywords set A is the superset of keywords set B's, it indicates A is a subset of B by a high chance. Most existing search methods that support CKS suffer from such inclusion-relation (IR) attacks. We define measurements on IR security and propose CKS-SE, a secure CKS scheme based on bloom filter that achieves IR-secure by randomizing and integrating expressions of trapdoors. Experiments show that the average false positives are within an acceptable rate, and the performance of CKS-SE is among the best ones.

To keep data confidential against unauthorized cloud servers and users, cryptographic access control mechanisms must be adopted. However, user revocation is a challenging issue since it would inevitably require data re-encryption, and may need user secret key updates. Considering the complexity of fine-grained access control policy and the large number of users in cloud, this issue would become extremely difficult to resolve. In this paper, we focus on this challenging open issue and present a secure and efficient revocation scheme. We propose a modified CP-ABE algorithm to set up a fine-grained access control method, in which user revocation is achieved based on the theory of Shamir's Secret Sharing. Compared with existing schemes, our scheme introduces a minimal overhead not only to the data owner but also to cloud servers. Collusions between cloud servers and revoked users can be avoided as long as the key-update protocol is honestly executed. Meanwhile, the data owner can delegate key updates to the cloud servers without disclosing data contents, user attributes, and the access policy information. Moreover, our scheme maintains the important feature that the revocation won't affect the users whose attribute set is a superset of the revoked user's.