Abstract Taking photos of sensitive facilities and sensitive information in no photography area may cause sensitive information leakage if not discovered in time. Employing action recognition models to detect instances of photography can effectively prevent information leakage. Current action recognition models have shown unsatisfactory performance in detecting photo-taking actions in surveillance videos, and their reliance on GPU devices hinder their practicality. This paper presents a novel approach to address the detection of photo-taking actions. The method utilizes object detection to filter out background data and incorporates human pose estimation to extract human skeleton data. By combining these AI techniques, the method enables accurate recognition of photo-taking actions. We introduce a novel technique called self-annotation that enables the model to focus on the crucial elements associated with photo-taking actions. Additionally, we introduce a new alarm mechanism that leads to a 69 $$\%$$ % reduction in false positives while maintaining the same level of recall by integrating the labels over a period to recognize actions. Compared with traditional action recognition approaches, our method is more flexible and lightweight in actual engineering applications. Moreover, our model is capable of running on CPU-only devices. Experimental results show that our model achieves a precision of 91 $$\%$$ % on our dataset.

Abstract Fuzzy extractors allow for the extraction and reproduction of a nearly uniform string from a noisy and non-uniform source. Reusable and robust fuzzy extractors further require that the output string should remain pseudorandom under multiple extractions and any modification of public value should be detectable. Existing constructions of reusable and robust fuzzy extractors are all designed in the Common Reference String (CRS) model and work only for CRS-independent sources. In this work, we introduce a construction of reusable and robust fuzzy extractor for CRS-dependent sources. Our construction is built upon some well-studied cryptography, including a collision resistant hash function, a symmetric key encryption that keeps secure with respect to auxiliary input, a public key encryption and simulation-sound non-interactive zero-knowledge argument. We also present some instantiations of these primitives from Learning with Parity Noise (LPN) assumption and Short Integer Solution (SIS) assumption. These instantiations result in the first reusable and robust fuzzy extractor for CRS-dependent sources that can tolerate linear errors.

The adoption of symmetric searchable encryption (SSE) has become increasingly common. However, many current SSE schemes assume an honest-but-curious cloud service provider (CSP) or necessitate significant overhead to manage a malicious CSP. Furthermore, most of these schemes are tailored for static datasets. Our paper presents an efficient SSE scheme that aims to address these challenges. To the best of our knowledge, this is the first scheme that supports dynamic datasets with forward and backward privacy, integrity verification of non-empty and empty search results, efficient search, non-interactive, light client, and both forward and inverted indexes simultaneously. In this paper, we present two novel approaches, Hexie and Jianding. Hexie implements secret sharing to conceal index entries, enabling dynamic updates, non-interactive interactions, and lightweight clients. To enhance the reliability of search results and address the problem of empty, incomplete, or inaccurate outcomes, we introduce the Jianding scheme as an extension of Hexie. It combines a chained MAC structure with a secret sharing scheme, which enables a client to verify the data integrity of the search result efficiently. Moreover, we propose graph-based dictionary sharding to enhance search efficiency. Finally, we conduct comprehensive experiments to validate the effectiveness of the proposed schemes.

Abstract The group fuzzy extractor allows group users to extract and reproduce group cryptographic keys from their individual non-uniform random sources. It can be easily used in group-oriented cryptographic applications. However, current group fuzzy extractors are not dynamic, i.e. they spend a large cost when dealing with user revocation. In this work, we propose the formal definition and construction of dynamic group fuzzy extractor (DGFE) to address this issue. For the revocation, DGFE allows unrevoked group users to reproduce updated group keys from the existing group help data. Meanwhile, it prevents any revoked group user from generating new group keys using the previously authorized individual help data. We propose a DGFE construction based on the revocable group signature. Furthermore, we give formal proofs of reusability, anonymity and traceability of our construction.

Cloud service models intrinsically cater to multiple tenants. In current multi-tenancy model, cloud service providers isolate data within a single tenant boundary with no or minimum cross-tenant interaction. With the booming of cloud applications, allowing a user to search across tenants is crucial to utilize stored data more effectively. However, conducting such a search operation is inherently risky, primarily due to privacy concerns. Moreover, existing schemes typically focus on a single tenant and are not well suited to extend support to a multi-tenancy cloud, where each tenant operates independently. In this article, to address the above issue, we provide a privacy-preserving, verifiable, accountable, and parallelizable solution for “privacy-preserving keyword search problem" among multiple independent data owners. We consider a scenario in which each tenant is a data owner and a user’s goal is to efficiently search for granted documents that contain the target keyword among all the data owners. We first propose a verifiable yet accountable keyword searchable encryption (VAKSE) scheme through symmetric bilinear mapping. For verifiability, a message authentication code (MAC) is computed for each associated piece of data. To maintain a consistent size of MAC, the computed MACs undergo an exclusive OR operation. For accountability, we propose a keyword-based accountable token mechanism where the client’s identity is seamlessly embedded without compromising privacy. Furthermore, we introduce the parallel VAKSE scheme, in which the inverted index is partitioned into small segments and all of them can be processed synchronously. We also conduct formal security analysis and comprehensive experiments to demonstrate the data privacy preservation and efficiency of the proposed schemes, respectively.

Abstract The handy biometric data is a double-edged sword, paving the way of the prosperity of biometric authentication systems but bringing the personal privacy concern. To alleviate the concern, various biometric template protection schemes are proposed to protect the biometric template from information leakage. The preponderance of existing proposals is based on Hamming metric, which ignores the fact that predominantly deployed biometric recognition systems (e.g. face, voice, gait) generate real-valued templates, more applicable to Euclidean metric and Cosine metric. Moreover, since the emergence of similarity-based attacks, those schemes are not secure under a stolen-token setting. In this paper, we propose a succinct biometric template protection scheme to address such a challenge. The proposed scheme is designed for Euclidean metric and Cosine metric instead of Hamming distance. Mainly, the succinct biometric template protection scheme consists of distance-preserving, one-way, and obfuscation modules. To be specific, we adopt location sensitive hash function to realize the distance-preserving and one-way properties simultaneously and use the modulo operation to implement many-to-one mapping. We also thoroughly analyze the proposed scheme in three aspects: irreversibility, unlinkability and revocability. Moreover, comprehensive experiments are conducted on publicly known face databases. All the results show the effectiveness of the proposed scheme.

Sample-then-lock construction is a reusable fuzzy extractor for low-entropy sources. When applied on iris recognition scenarios, many subsets of an iris-code are used to lock the cryptographic key. The security of this construction relies on the entropy of subsets of iris codes. Simhadri et al. reported a security level of 32 bits on iris sources. In this paper, we propose two kinds of attacks to crack existing sample-then-lock schemes. Exploiting the low-entropy subsets, our attacks can break the locked key and the enrollment iris-code respectively in less than 220brute force attempts. To protect from these proposed attacks, we design an improved sample-then-lock scheme. More precisely, our scheme employs stability and discriminability to select high-entropy subsets to lock the genuine secret, and conceals genuine locker by a large amount of chaff lockers. Our experiment verifies that existing schemes are vulnerable to the proposed attacks with a security level of less than 20 bits, while our scheme can resist these attacks with a security level of more than 100 bits when number of genuine subsets is 106.

Iris segmentation is a critical part in iris recognition systems. It segments the acquired image into iris and non-iris parts. It is the foundation of subsequent processing. The errors in this stage are propagated to subsequent processing stages, which will affecting the recognition rate of the whole system. A majority of iris segmentation algorithms require a significant amount of user cooperation during image acquisition process to provide good segmentation performance. However, the quality of iris images can not be guaranteed. When an iris image is acquired under non-ideal conditions (e.g., bad illumination, uncooperative subject, occluded iris, etc.), segmentation becomes a challenging task. In this paper, we present a more robust iris segmentation method using fully convolutional network (FCN) with dilated convolutions. We reduce the downsampling factor of the FCN model, and use the dilated convolutions to extract the more global features, which makes our method better at dealing with details. Moreover, our model supports end to end prediction, it does not need any pre-processing, such as adjusting the image to a fixed size. We used three datasets for training and testing, including CASIA-iris-interval-v4, UBIRIS v2 and IITD Delhi datasets. Experiments show that our model greatly reduced the error rate of the current state-of-the-arts by 79%, 84% and 79% on the CASIA-iris-interval-v4, IITD Delhi and UBIRIS v2 datasets respectively.

Cloud data owners prefer to outsource documents in an encrypted form for the purpose of privacy preserving. Therefore it is essential to develop efficient and reliable ciphertext search techniques. One challenge is that the relationship between documents will be normally concealed in the process of encryption, which will lead to significant search accuracy performance degradation. Also the volume of data in data centers has experienced a dramatic growth. This will make it even more challenging to design ciphertext search schemes that can provide efficient and reliable online information retrieval on large volume of encrypted data. In this paper, a hierarchical clustering method is proposed to support more search semantics and also to meet the demand for fast ciphertext search within a big data environment. The proposed hierarchical approach clusters the documents based on the minimum relevance threshold, and then partitions the resulting clusters into sub-clusters until the constraint on the maximum size of cluster is reached. In the search phase, this approach can reach a linear computational complexity against an exponential size increase of document collection. In order to verify the authenticity of search results, a structure called minimum hash sub-tree is designed in this paper. Experiments have been conducted using the collection set built from the IEEE Xplore. The results show that with a sharp increase of documents in the dataset the search time of the proposed method increases linearly whereas the search time of the traditional method increases exponentially. Furthermore, the proposed method has an advantage over the traditional method in the rank privacy and relevance of retrieved documents.