Outsourcing Set Intersection Computation Based on Bloom Filter for Privacy Preservation in Multimedia Processing

Zhu, Hongliang; Chen, Meiqi; Sun, Maohua; Liao, Xin; Hu, Lei

doi:10.1155/2018/5841967

Cited by 9 publications

(5 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Table 2 , s in classic algorithms represent the data scale. Table 2 shows: (1) Comparing with the classic PSI-CA protocols, The computational complexity and communication complexity will increase linearly with the increasing data scale, such as Huang’s 11 scheme, Dong’s 25 , Kerschbaum’s 39 , and Zhu’s 41 . So if the data scale are too large, the complexity will increase linearly and the efficiency will be greatly reduced.…”

Section: Performancementioning

confidence: 99%

Quantum private set intersection cardinality based on bloom filter

Liu

Ruan

Shi

et al. 2021

Sci Rep

View full text Add to dashboard Cite

Private Set Intersection Cardinality that enable Multi-party to privately compute the cardinality of the set intersection without disclosing their own information. It is equivalent to a secure, distributed database query and has many practical applications in privacy preserving and data sharing. In this paper, we propose a novel quantum private set intersection cardinality based on Bloom filter, which can resist the quantum attack. It is a completely novel constructive protocol for computing the intersection cardinality by using Bloom filter. The protocol uses single photons, so it only need to do some simple single-photon operations and tests. Thus it is more likely to realize through the present technologies. The validity of the protocol is verified by comparing with other protocols. The protocol implements privacy protection without increasing the computational complexity and communication complexity, which are independent with data scale. Therefore, the protocol has a good prospects in dealing with big data, privacy-protection and information-sharing, such as the patient contact for COVID-19.

show abstract

Section: Performancementioning

confidence: 99%

Quantum private set intersection cardinality based on bloom filter

Liu

Ruan

Shi

et al. 2021

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Zhu et al (2018) introduced a novel privacy-preserving outsourcing computation method that integrated Goldwasser–Micali homomorphic encryption with the Bloom filter to address the scalability issue to provide a new privacy-preserving protocol. Despite improvement was observed, it did not address the incremental data update issue.…”

Section: Literature Review Of Related Workmentioning

confidence: 99%

Deep restricted and additive homomorphic ElGamal privacy preservations over big healthcare data

Sujatha

Udayarani

2021

IJICC

View full text Add to dashboard Cite

PurposeThe purpose of this paper is to improve the privacy in healthcare datasets that hold sensitive information. Putting a stop to privacy divulgence and bestowing relevant information to legitimate users are at the same time said to be of differing goals. Also, the swift evolution of big data has put forward considerable ease to all chores of life. As far as the big data era is concerned, propagation and information sharing are said to be the two main facets. Despite several research works performed on these aspects, with the incremental nature of data, the likelihood of privacy leakage is also substantially expanded through various benefits availed of big data. Hence, safeguarding data privacy in a complicated environment has become a major setback.Design/methodology/approachIn this study, a method called deep restricted additive homomorphic ElGamal privacy preservation (DR-AHEPP) to preserve the privacy of data even in case of incremental data is proposed. An entropy-based differential privacy quasi identification and DR-AHEPP algorithms are designed, respectively, for obtaining privacy-preserved minimum falsified quasi-identifier set and computationally efficient privacy-preserved data.FindingsAnalysis results using Diabetes 130-US hospitals illustrate that the proposed DR-AHEPP method is more significant in preserving privacy on incremental data than existing methods. A comparative analysis of state-of-the-art works with the objective to minimize information loss, false positive rate and execution time with higher accuracy is calibrated.Originality/valueThe paper provides better performance using Diabetes 130-US hospitals for achieving high accuracy, low information loss and false positive rate. The result illustrates that the proposed method increases the accuracy by 4% and reduces the false positive rate and information loss by 25 and 35%, respectively, as compared to state-of-the-art works.

show abstract

“…For example, refs. [2][3][4]9] considered outsourcing set intersection operations to the cloud while preserving the privacy of the data sets. In addition, symmetric/asymmetric encryption with equality test, e.g., [10,11], is a specific solution for one-element data sets.…”

Section: Schemesmentioning

confidence: 99%

Ciphertext-Policy Attribute-Based Encryption with Outsourced Set Intersection in Multimedia Cloud Computing

Shi

Qiu

2021

Electronics

View full text Add to dashboard Cite

In a multimedia cloud computing system, suppose all cloud users outsource their own data sets to the cloud in the encrypted form. Each outsourced set is associated with an access structure such that a valid data user, Bob, with the credentials satisfying the access structure is able to conduct computing over outsourced encrypted set (e.g., decryption or other kinds of computing function). Suppose Bob needs to compute the set intersection over a data owner Alice’s and his own outsourced encrypted sets. Bob’s simple solution is to download Alice’s and Bob’s outsourced encrypted sets, perform set intersection operation, and decrypt the set intersection ciphertexts. A better solution is for Bob to delegate the cloud to calculate the set intersection, without giving the cloud any ability in breaching the secrecy of the sets. To solve this problem, this work introduces a novel primitive called ciphertext-policy attribute-based encryption with outsourced set intersection for multimedia cloud computing. It is the first cryptographic algorithm supporting a fully outsourced encrypted storage, computation delegation, fine-grained authorization security for ciphertext-policy model, without relying on an online trusted authority or data owners, and multi-elements set, simultaneously. We construct a scheme that provably satisfies the desirable security properties, and analyze its efficiency.

show abstract

Outsourcing Set Intersection Computation Based on Bloom Filter for Privacy Preservation in Multimedia Processing

Cited by 9 publications

References 25 publications

Quantum private set intersection cardinality based on bloom filter

Quantum private set intersection cardinality based on bloom filter

Deep restricted and additive homomorphic ElGamal privacy preservations over big healthcare data

Ciphertext-Policy Attribute-Based Encryption with Outsourced Set Intersection in Multimedia Cloud Computing

Contact Info

Product

Resources

About