Secure data forwarding involves converting decrypted ciphertext, initially readable by one user, into a format that can be deciphered by another user. Proxy re-encryption is a commonly employed technique for secure data forwarding. However, this technique faces two inherent limitations. Firstly, only the data owner possesses the ability to control which data users can decrypt the ciphertext, resulting in receivers receiving irrelevant or uninterested information. Secondly, when data is forwarded through multiple nodes, it becomes vulnerable to various attacks such as impersonation and forgery. A solution called bilateral access control addresses these issues by letting the sender and receiver specify access control policies that the other party should comply with and ensure message confidentiality and authenticity. Nevertheless, to the best of our knowledge, there is currently no existing bilateral access control scheme capable of achieving secure data forwarding. In response to this issue, we propose a privacy-preserving proxy bilateral access control scheme, which simultaneously achieves all the above functionalities. Subsequently, we prove the message confidentiality and authenticity under the standard assumptions in the random oracle model. Finally, extensive theoretical analysis and performance evaluation demonstrate that the scheme provides unique features and comparable performance.

Machine learning (ML), driven by prominent paradigms such as centralized and federated learning, has made significant progress in various critical applications. However, its remarkable success has been accompanied by various attacks. Recently, the model hijacking attack has shown that ML models can be hijacked to execute tasks different from their original tasks, which increases both accountability and parasitic computational risks. Nevertheless, thus far, this attack has only focused on centralized learning. In this work, we broaden the scope of this attack to the federated learning domain, where multiple clients collaboratively train a global model without sharing their data. Specifically, we present the first-of-its-kind hijacking attack against the global model in federated learning, namely HijackFL. The adversary aims to force the global model to perform a different task (called hijacking task) from its original task without the server or benign client noticing. To accomplish this, unlike existing methods that use data poisoning to modify the target model’s parameters, HijackFL searches for pixel-level perturbations based on their local model (without modifications) to align hijacking samples with the original ones in the feature space. When performing the hijacking task, the adversary applies these perturbations to the hijacking samples, compelling the global model to identify them as original ones and predict them accordingly. Extensive experiments demonstrate HijackFL significantly outperforms baselines, e.g., 92.75% vs. 10%. We further investigate the factors that affect its performance and discuss possible defenses to mitigate its impact.

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.

Most existing membership inference attacks (MIAs) utilize metrics (e.g., loss) calculated on the model's final state, while recent advanced attacks leverage metrics computed at various stages, including both intermediate and final stages, throughout the model training. Nevertheless, these attacks often process multiple intermediate states of the metric independently, ignoring their time-dependent patterns. Consequently, they struggle to effectively distinguish between members and non-members who exhibit similar metric values, particularly resulting in a high false-positive rate.

Mobile social networks (MSNs) offer convenient and ubiquitous services to expand social circles, share information, etc. These services require strict security measures to prevent the spread of deceptive content, misleading information, and malicious behavior. Achieving bilateral access control, message confidentiality and authenticity, and identity privacy can establish a positive network environment. Identity-based matchmaking encryption (IB-ME) with all the above features is a promising cryptographic primitive for MSNs. However, IB-ME can only specify one receiver. To share data with multiple users, the sender needs to encrypt the same message many times, resulting in higher frequencies of communication. Moreover, in multi-receiver scenarios, revocation of decryption permission may be necessary due to the possibility of malicious behavior, organization changes, or discontinuing subscription services. To our knowledge, no cryptographic primitives have been developed that satisfy these requirements. To address these challenges, we introduce the concept of revocable multi-receiver IB-ME and formalize its syntax and security definitions. We propose a revocable multi-receiver IB-ME scheme that provides privacy and authenticity in the random oracle model. Our evaluation demonstrates that it is efficient, and the sizes of system parameters and secret keys are independent of the number of receivers and revoked receivers.

By adding noise to real data locally and providing quantitative privacy protection that can be rigorously mathematically proven, Local differential privacy is the suitable technology for the private collection of two dimensional location data. Most current solutions discretize the location information into grids, and then apply LDP-based frequency oracle to obtain distribution information of all users for spatial range query. However, the discretization step of gridding will result in a more or less loss of accuracy, while eliminating the inherent correlation between adjacent grids. Thus leading to a large overall error. Drawing on the idea of continuous perturbation on finite intervals, we propose a two-dimensional continuous density estimation method, called LTD-EM. It takes advantage of numerical nature of the map domain and uses the near-neighbor perturbation and EM algorithm. We also optimize the algorithm considering the irregular shape of the geography map. The experimental results show that the accuracy of the spatial range query provided by LTD-EM is significantly better than that of existing solutions.

Traditional role-based access control fails to meet the privacy requirements for patient data in medical systems, as it is infeasible for policy makers to foresee what information doctors may need for diagnosis and treatment in various situations. The universal practice in hospitals is to grant doctors unlimited access, which in turn increases the risk of breaching patient privacy. In this paper, we propose a dynamic risk-adaptive access control model for health IT systems by taking into consideration the relationships between data and access behaviors. By training topic models to portray individual and group-level access behaviors, we quantify the risk for each user over a certain period of time. Malicious users are supposed to get higher risk scores than honest users due to improper requests. Thus their further access would be denied under our access control scheme. The topic model and risk scores are periodically updated to advance the self-adaptability of the system. Experimental results have shown that our solution could effectively distinguish malicious doctors even if they deliberately conceal the misconducts.

Data encryption is an effective way to ensure the data security in the cloud. To make retrieval of such encrypted data easy for multiple users, searchable encryption in the multi-user setting is addressed. However, it introduces a new critical requirement: access control. Cipher text-Policy Attribute-Based Encryption (CP-ABE) is a promising technique to solve this issue, but it also faces several challenges, such as the inefficiency of decrypt able files search, attributes verification and decryption. In this paper, we propose a multiuser searchable encryption scheme with efficient access control for cloud storage, where the keyword index and trapdoor can be generated with the help of a proxy server. To achieve the efficient access control, we present the first solution to search the data that a user can decrypt by using the partial order relations. We also design a new method to verify each user's attributes without disclosing the relation of his identity and attributes. To reduce the decryption overhead, our scheme enables the users to delegate most CP-ABE decryption to the proxy server. Moreover, the security analysis and simulation results show that the proposed scheme is provably secure and highly efficient.

Similar with other outsourced services, cloud storage faces the serious issue of user data security. To keep data confidential against unauthorized cloud servers and users, Attribute-Based Encryption (ABE) for access control is widely adopted. However, ABE-based access control schemes are being criticized for their high computation overhead, such as in key generation, decryption and revocation. Considering the mobile cloud storage environment where these computation tasks are executed by mobile devices or sensors, this drawback appears more serious. In this paper, we propose an efficient and secure attribute-based access control scheme for mobile cloud storage. Specifically, we construct the first Key-Policy ABE (KP-ABE) scheme with outsourced key generation and decryption, and propose an efficient revocation method for it. Moreover, we prove the proposed scheme is immune to the collusion attack and secure in the standard model. Extensive experiment demonstrates that the efficient key generation, decryption, and revocation are achieved with the help of the cloud servers.

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.

Recovery from information attacks is difficult because DBMS is not designed to deal with malicious committed transactions. A few existing methods developed for this purpose rely on operation logs, which can't express the dependency between different transactions directly. These methods usually use rollback mechanism and abandon results of innocent transactions to maintain correctness, which may indeed be used as an approach to realize DOS attack. Hence, it's necessary to find out the malicious transaction and subsequent transactions depending on it precisely. In this paper, the definition of transaction recovery log is presented and each log item records the actions taken in one transaction, by which, we can calculate transactions' dependency directly. Based on the log model and the algorithm for log's creation, the dependency calculation and data recovery algorithm are studied, which are proofed to be complete and correct. Using transaction recovery log and the algorithm, database system can significantly enhance the performance of recovery for defensive information warfare.