Comment on "An Efficient Privacy-Preserving Ranked Multi-Keyword Retrieval for Multiple Data Owners in Outsourced Cloud"

• URL: http://arxiv.org/abs/2408.05218v1
• Date: Thu, 25 Jul 2024 05:01:07 GMT
• Title: Comment on "An Efficient Privacy-Preserving Ranked Multi-Keyword Retrieval for Multiple Data Owners in Outsourced Cloud"
• Authors: Uma Sankararao Varri,
• Abstract summary: We show that the scheme fails to resist keyword guessing attack, index privacy, and trapdoor privacy. We propose a solution to address the above said issues by correcting the errors in the important equations of the scheme.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Protecting the privacy of keywords in the field of search over outsourced cloud data is a challenging task. In IEEE Transactions on Services Computing (Vol. 17 No. 2, March/April 2024), Li et al. proposed PRMKR: efficient privacy-preserving ranked multi-keyword retrieval scheme, which was claimed to resist keyword guessing attack. However, we show that the scheme fails to resist keyword guessing attack, index privacy, and trapdoor privacy. Further, we propose a solution to address the above said issues by correcting the errors in the important equations of the scheme.

Related papers

• Subword Embedding from Bytes Gains Privacy without Sacrificing Accuracy and Complexity [5.7601856226895665]
  We propose Subword Embedding from Bytes (SEB) and encode subwords to byte sequences using deep neural networks. Our solution outperforms conventional approaches by preserving privacy without sacrificing efficiency or accuracy. We verify SEB obtains comparable and even better results over standard subword embedding methods in machine translation, sentiment analysis, and language modeling.
  arXiv  Detail & Related papers  (2024-10-21T18:25:24Z)
• Speech privacy-preserving methods using secret key for convolutional neural network models and their robustness evaluation [5.762345156477736]
  In environments where untrusted third parties provide CNN-based systems, ensuring the privacy of speech queries becomes essential. This paper proposes encryption methods for speech queries using secret keys and a model structure that allows for encrypted queries to be accepted without decryption.
  arXiv  Detail & Related papers  (2024-08-07T16:51:39Z)
• Robust Utility-Preserving Text Anonymization Based on Large Language Models [80.5266278002083]
  Text anonymization is crucial for sharing sensitive data while maintaining privacy. Existing techniques face the emerging challenges of re-identification attack ability of Large Language Models. This paper proposes a framework composed of three LLM-based components -- a privacy evaluator, a utility evaluator, and an optimization component.
  arXiv  Detail & Related papers  (2024-07-16T14:28:56Z)
• d-DSE: Distinct Dynamic Searchable Encryption Resisting Volume Leakage in Encrypted Databases [24.259108931623203]
  Dynamic Searchable Encryption (DSE) has emerged as a solution to efficiently handle and protect large-scale data storage in encrypted databases (EDBs) Volume leakage poses a significant threat, as it enables adversaries to reconstruct search queries and potentially compromise the security and privacy of data. Padding strategies are common countermeasures for the leakage, but they significantly increase storage and communication costs.
  arXiv  Detail & Related papers  (2024-03-02T11:42:17Z)
• Blockchain-based Privacy-Preserving Public Key Searchable Encryption with Strong Traceability [8.866154405581012]
  We propose a blockchain-based privacy-preserving PKSE with strong traceability (BP3KSEST) scheme. Our scheme provides the following features: (1) authorized users can authenticate to trapdoor generation center and obtain trapdoors without releasing identities and keywords; (2) when data users misbehave in the system, the trusted third party (TTP) can trace both their identities and the keywords which they searched; (3) trapdoor query records are unforgeable; (4) trapdoor query records are immutable because records are stored in blockchain.
  arXiv  Detail & Related papers  (2023-12-28T10:58:14Z)
• Leakage-Abuse Attacks Against Forward and Backward Private Searchable Symmetric Encryption [13.057964839510596]
  Dynamic searchable encryption (DSSE) enables a server to efficiently search and update over encrypted files. To minimize the leakage during updates, a security notion named forward and backward privacy is expected for newly proposed DSSE schemes. It remains underexplored whether forward and backward private DSSE is resilient against practical leakage-abuse attacks (LAAs)
  arXiv  Detail & Related papers  (2023-09-09T06:39:35Z)
• To Wake-up or Not to Wake-up: Reducing Keyword False Alarm by Successive Refinement [58.96644066571205]
  We show that existing deep keyword spotting mechanisms can be improved by Successive Refinement. We show across multiple models with size ranging from 13K parameters to 2.41M parameters, the successive refinement technique reduces FA by up to a factor of 8. Our proposed approach is "plug-and-play" and can be applied to any deep keyword spotting model.
  arXiv  Detail & Related papers  (2023-04-06T23:49:29Z)
• Efficient Image-Text Retrieval via Keyword-Guided Pre-Screening [53.1711708318581]
  Current image-text retrieval methods suffer from $N$-related time complexity. This paper presents a simple and effective keyword-guided pre-screening framework for the image-text retrieval.
  arXiv  Detail & Related papers  (2023-03-14T09:36:42Z)
• On Differential Privacy and Adaptive Data Analysis with Bounded Space [76.10334958368618]
  We study the space complexity of the two related fields of differential privacy and adaptive data analysis. We show that there exists a problem P that requires exponentially more space to be solved efficiently with differential privacy. The line of work on adaptive data analysis focuses on understanding the number of samples needed for answering a sequence of adaptive queries.
  arXiv  Detail & Related papers  (2023-02-11T14:45:31Z)
• Smooth Anonymity for Sparse Graphs [69.1048938123063]
  differential privacy has emerged as the gold standard of privacy, however, when it comes to sharing sparse datasets. In this work, we consider a variation of $k$-anonymity, which we call smooth-$k$-anonymity, and design simple large-scale algorithms that efficiently provide smooth-$k$-anonymity.
  arXiv  Detail & Related papers  (2022-07-13T17:09:25Z)
• Semantic-Preserving Adversarial Text Attacks [85.32186121859321]
  We propose a Bigram and Unigram based adaptive Semantic Preservation Optimization (BU-SPO) method to examine the vulnerability of deep models. Our method achieves the highest attack success rates and semantics rates by changing the smallest number of words compared with existing methods.
  arXiv  Detail & Related papers  (2021-08-23T09:05:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.