Privacy-Preserving Genetic Relatedness Test

An increasing number of individuals are turning to Direct-To-Consumer (DTC) genetic testing to learn about their predisposition to diseases, traits, and/or ancestry. DTC companies like 23andme and Ancestry.com have started to offer popular and affordable ancestry and genealogy tests, with services allowing users to find unknown relatives and long-distant cousins. Naturally, access and possible dissemination of genetic data prompts serious privacy concerns, thus motivating the need to design efficient primitives supporting private genetic tests. In this paper, we present an effective protocol for privacy-preserving genetic relatedness test (PPGRT), enabling a cloud server to run relatedness tests on input an encrypted genetic database and a test facility's encrypted genetic sample. We reduce the test to a data matching problem and perform it, privately, using searchable encryption. Finally, a performance evaluation of hamming distance based PP-GRT attests to the practicality of our proposals.Comment: A preliminary version of this paper appears in the Proceedings of the 3rd International Workshop on Genome Privacy and Security (GenoPri'16

Searchable atribute-based mechanism with efficiient data sharing for secure cloud storage

To date, the growth of electronic personal data leads to a trend that data owners prefer to remotely outsource their data to clouds for the enjoyment of the high-quality retrieval and storage service without worrying the burden of local data management and maintenance. However, secure share and search for the outsourced data is a formidable task, which may easily incur the leakage of sensitive personal information. Efficient data sharing and searching with security is of critical importance. This paper, for the first time, proposes a searchable attribute-based proxy re-encryption system. When compared to existing systems only supporting either searchable attribute-based functionality or attribute-based proxy re-encryption, our new primitive supports both abilities and provides flexible keyword update service. Specifically, the system enables a data owner to efficiently share his data to a specified group of users matching a sharing policy and meanwhile, the data will maintain its searchable property but also the corresponding search keyword(s) can be updated after the data sharing. The new mechanism is applicable to many real-world applications, such as electronic health record systems. It is also proved chosen ciphertext secure in the random oracle model

FTA: Stealthy and Robust Backdoor Attack with Flexible Trigger on Federated Learning

Current backdoor attacks against federated learning (FL) strongly rely on universal triggers or semantic patterns, which can be easily detected and filtered by certain defense mechanisms such as norm clipping, comparing parameter divergences among local updates. In this work, we propose a new stealthy and robust backdoor attack with flexible triggers against FL defenses. To achieve this, we build a generative trigger function that can learn to manipulate the benign samples with an imperceptible flexible trigger pattern and simultaneously make the trigger pattern include the most significant hidden features of the attacker-chosen label. Moreover, our trigger generator can keep learning and adapt across different rounds, allowing it to adjust to changes in the global model. By filling the distinguishable difference (the mapping between the trigger pattern and target label), we make our attack naturally stealthy. Extensive experiments on real-world datasets verify the effectiveness and stealthiness of our attack compared to prior attacks on decentralized learning framework with eight well-studied defenses

File-Injection Attacks on Searchable Encryption, Based on Binomial Structures

One distinguishable feature of file-inject attacks on searchable encryption schemes is the 100% query recovery rate, i.e., confirming the corresponding keyword for each query. The main efficiency consideration of file-injection attacks is the number of injected files. In the work of Zhang et al. (USENIX 2016), $|\log_2|K||$ injected files are required, each of which contains $|K|/2$ keywords for the keyword set $K$. Based on the construction of the uniform $(s,n)$-set, Wang et al. need fewer injected files when considering the threshold countermeasure. In this work, we propose a new attack that further reduces the number of injected files where Wang et al. need up to 38% more injections to achieve the same results. The attack is based on an increment $(s,n)$-set, which is also defined in this paper

CCA-1 Secure Updatable Encryption with Adaptive Security

Updatable encryption (UE) enables a cloud server to update ciphertexts using client-generated tokens. There are two types of UE: ciphertext-independent (c-i) and ciphertext-dependent (c-d). In terms of construction and efficiency, c-i UE utilizes a single token to update all ciphertexts. The update mechanism relies mainly on the homomorphic properties of exponentiation, which limits the efficiency of encryption and updating. Although c-d UE may seem inconvenient as it requires downloading parts of the ciphertexts during token generation, it allows for easy implementation of the Dec-then-Enc structure. This methodology significantly simplifies the construction of the update mechanism. Notably, the c-d UE scheme proposed by Boneh et al. (ASIACRYPT’20) has been reported to be 200 times faster than prior UE schemes based on DDH hardness, which is the case for most existing c-i UE schemes. Furthermore, c-d UE ensures a high level of security as the token does not reveal any information about the key, which is difficult for c-i UE to achieve. However, previous security studies on c-d UE only addressed selective security; the studies for adaptive security remain an open problem. In this study, we make three significant contributions to ciphertextdependent updatable encryption (c-d UE). Firstly, we provide stronger security notions compared to previous work, which capture adaptive security and also consider the adversary’s decryption capabilities under the adaptive corruption setting. Secondly, we propose a new c-d UE scheme that achieves the proposed security notions. The token generation technique significantly differs from the previous Dec-then-Enc structure, while still preventing key leakages. At last, we introduce a packing technique that enables the simultaneous encryption and updating of multiple messages within a single ciphertext. This technique helps alleviate the cost of c-d UE by reducing the need to download partial ciphertexts during token generation

Inject Less, Recover More: Unlocking the Potential of Document Recovery in Injection Attacks Against SSE

Searchable symmetric encryption has been vulnerable to inference attacks that rely on uniqueness in leakage patterns. However, many keywords in datasets lack distinctive leakage patterns, limiting the effectiveness of such attacks. The file injection attacks, initially proposed by Cash et al. (CCS 2015), have shown impressive performance with 100% accuracy and no prior knowledge requirement. Nevertheless, this attack fails to recover queries with underlying keywords not present in the injected files. To address these limitations, our research introduces a novel attack strategy called LEAP-Hierarchical Fusion Attack (LHFA) that combines the strengths of both file injection attacks and inference attacks. Before initiating keyword injection, we introduce a new approach for inert/active keyword selection. In the phase of selecting injected keywords, we focus on keywords without unique leakage patterns and recover them, leveraging their presence for document recovery. Our goal is to achieve an amplified effect in query recovery. We demonstrate a minimum query recovery rate of 1.3 queries per injected keyword with a 10% data leakage of a real-life dataset, and initiate further research to overcome challenges associated with non-distinctive keywords

Volume and Access Pattern Leakage-abuse Attack with Leaked Documents

Searchable Encryption schemes provide secure search over encrypted databases while allowing admitted information leakages. Generally, the leakages can be categorized into access and volume pattern. In most existing SE schemes, these leakages are caused by practical designs but are considered an acceptable price to achieve high search efficiency. Recent attacks have shown that such leakages could be easily exploited to retrieve the underlying keywords for search queries. Under the umbrella of attacking SE, we design a new Volume and Access Pattern Leakage-Abuse Attack (VAL-Attack) that improves the matching technique of LEAP (CCS ’21) and exploits both the access and volume patterns. Our proposed attack only leverages leaked documents and the keywords present in those documents as auxiliary knowledge and can effectively retrieve document and keyword matches from leaked data. Furthermore, the recovery performs without false positives. We further compare VAL-Attack with two recent well-defined attacks on several real-world datasets to highlight the effectiveness of our attack and present the performance under popular countermeasures