Approaches and challenges of privacy preserving search over encrypted data

Kumar, Deepak; Thilagam, P. Santhi

doi:10.1016/j.is.2018.11.004

Cited by 10 publications

(2 citation statements)

References 39 publications

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“…Searchable encryption is defined as searching for encrypted data located on a server or cloud without the server learning anything from the data [ 6 ]. This search procedure assumes an encryption scheme that allows for the sending of a collection of encrypted data to the server while supporting keyword searches on these data [ 5 , 7 , 8 , 9 ]. The current solutions for searchable encryption either use an encryption algorithm that allows search operations to be performed on the ciphertext or an index that is created based on existing keywords.…”

Section: Related Work On Searchable Encryption and Performancementioning

confidence: 99%

On the Performance of Secure Sharing of Classified Threat Intelligence between Multiple Entities

Fernandes

Bugla

Pinto

et al. 2023

Sensors

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The sharing of cyberthreat information within a community or group of entities is possible due to solutions such as the Malware Information Sharing Platform (MISP). However, the MISP was considered limited if its information was deemed as classified or shared only for a given period of time. A solution using searchable encryption techniques that better control the sharing of information was previously proposed by the same authors. This paper describes a prototype implementation for two key functionalities of the previous solution, considering multiple entities sharing information with each other: the symmetric key generation of a sharing group and the functionality to update a shared index. Moreover, these functionalities are evaluated regarding their performance, and enhancements are proposed to improve the performance of the implementation regarding its execution time. As the main result, the duration of the update process was shortened from around 2922 s to around 302 s, when considering a shared index with 100,000 elements. From the security analysis performed, the implementation can be considered secure, thus confirming the secrecy of the exchanged nonces. The limitations of the current implementation are depicted, and future work is pointed out.

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Section: Related Work On Searchable Encryption and Performancementioning

confidence: 99%

On the Performance of Secure Sharing of Classified Threat Intelligence between Multiple Entities

Fernandes

Bugla

Pinto

et al. 2023

Sensors

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show abstract

“…These DL-based applications usually demand the gathering of large quantities of data from various IoT edge-devices for training high-quality learning models. However, the traditionally centralised DL models require the local edge-devices to upload their private data to a central cloud server, which may cause serious privacy threats [5]. These privacy threats can be mitigated through distributing the local training among multiple edge-devices, which has led to the emergence of Federated Learning (FL) [6].…”

Section: Introductionmentioning

confidence: 99%

FedOpt: Towards Communication Efficiency and Privacy Preservation in Federated Learning

2020

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Artificial Intelligence (AI) has been applied to solve various challenges of real-world problems in recent years. However, the emergence of new AI technologies has brought several problems, especially with regard to communication efficiency, security threats and privacy violations. Towards this end, Federated Learning (FL) has received widespread attention due to its ability to facilitate the collaborative training of local learning models without compromising the privacy of data. However, recent studies have shown that FL still consumes considerable amounts of communication resources. These communication resources are vital for updating the learning models. In addition, the privacy of data could still be compromised once sharing the parameters of the local learning models in order to update the global model. Towards this end, we propose a new approach, namely, Federated Optimisation (FedOpt) in order to promote communication efficiency and privacy preservation in FL. In order to implement FedOpt, we design a novel compression algorithm, namely, Sparse Compression Algorithm (SCA) for efficient communication, and then integrate the additively homomorphic encryption with differential privacy to prevent data from being leaked. Thus, the proposed FedOpt smoothly trade-offs communication efficiency and privacy preservation in order to adopt the learning task. The experimental results demonstrate that FedOpt outperforms the state-of-the-art FL approaches. In particular, we consider three different evaluation criteria; model accuracy, communication efficiency and computation overhead. Then, we compare the proposed FedOpt with the baseline configurations and the state-of-the-art approaches, i.e., Federated Averaging (FedAvg) and the paillier-encryption based privacy-preserving deep learning (PPDL) on all these three evaluation criteria. The experimental results show that FedOpt is able to converge within fewer training epochs and a smaller privacy budget.

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Interpretable Encrypted Searchable Neural Networks

Chen

Lin

Wan

et al. 2019

Lecture Notes in Computer Science

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In cloud security, traditional searchable encryption (SE) requires high computation and communication overhead for dynamic search and update. The clever combination of machine learning (ML) and SE may be a new way to solve this problem. This paper proposes interpretable encrypted searchable neural networks (IESNN) to explore probabilistic query, balanced index tree construction and automatic weight update in an encrypted cloud environment. In IESNN, probabilistic learning is used to obtain search ranking for searchable index, and probabilistic query is performed based on ciphertext index, which reduces the computational complexity of query significantly. Compared to traditional SE, it is proposed that adversarial learning and automatic weight update in response to user's timely query of the latest data set without expensive communication overhead. The proposed IESNN performs better than the previous works, bringing the query complexity closer to O(log N ) and introducing low overhead on computation and communication.

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Approaches and challenges of privacy preserving search over encrypted data

Cited by 10 publications

References 39 publications

On the Performance of Secure Sharing of Classified Threat Intelligence between Multiple Entities

On the Performance of Secure Sharing of Classified Threat Intelligence between Multiple Entities

FedOpt: Towards Communication Efficiency and Privacy Preservation in Federated Learning

Interpretable Encrypted Searchable Neural Networks

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