Controlled Functional Encryption

Naveed, Muhammad; Agrawal, Shashank; Prabhakaran, Manoj; Wang, Xiaofeng; Ayday, Erman; Hubaux, Jean-Pierre; Gunter, Carl A.

doi:10.1145/2660267.2660291

Cited by 46 publications

(41 citation statements)

References 46 publications

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“…Other than providing one-timeness, the proposed scheme also provides (i) non-interactivity, in which the user does not need to interact with the vendor during the protocol, and (ii) pattern-hiding, which ensures that the patterns used in vendor's test are kept private from the user. On the other hand, homomorphic encryption-based schemes [3] lack noninteractivity and functional encryption-based schemes [46] lack non-interactivity and pattern-hiding. We did not specifically implement these other techniques and compare our solution with them.…”

Section: Case Studymentioning

confidence: 99%

One-Time Programs Made Practical

Zhao

Choi

Demirag

et al. 2019

Financial Cryptography and Data Security

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A one-time program (OTP) works as follows: Alice provides Bob with the implementation of some function. Bob can have the function evaluated exclusively on a single input of his choosing. Once executed, the program will fail to evaluate on any other input. State-of-the-art one-time programs have remained theoretical, requiring custom hardware that is cost-ineffective/unavailable, or confined to adhoc/unrealistic assumptions. To bridge this gap, we explore how the Trusted Execution Environment (TEE) of modern CPUs can realize the OTP functionality. Specifically, we build two flavours of such a system: in the first, the TEE directly enforces the one-timeness of the program; in the second, the program is represented with a garbled circuit and the TEE ensures Bob's input can only be wired into the circuit once, equivalent to a smaller cryptographic primitive called one-time memory. These have different performance profiles: the first is best when Alice's input is small and Bob's is large, and the second for the converse. 5 Hazay and Lindell [24] give a thorough treatment of interactive two-party protocols. 6 Further explanation is provided in Appendix A.2. arXiv:1907.00935v1 [cs.CR] 1 Jul 2019 7 As an example, Intel STK2mv64CC, a Compute Stick that supports both TXT and TPM, was priced at $499.95 USD on Amazon.com (as of September 2018). 8 A state-bound cryptographic operation performed by the TPM chip, like encryption.

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Section: Case Studymentioning

confidence: 99%

One-Time Programs Made Practical

Zhao

Choi

Demirag

et al. 2019

Financial Cryptography and Data Security

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“…18 Furthermore, Muhammad Naveed and his colleagues proposed a scheme based on functional encryption for privacy-preserving similarity tests on genomic data. 19 Recently, Xiao Shaun Wang and his colleagues proposed an efficient privacy-preserving protocol to find genetically similar patients in a distributed environment. 20 Other works have focused on private clinical genomics.…”

Section: Cryptography-based Solutionsmentioning

confidence: 99%

Inference Attacks against Kin Genomic Privacy

Ayday¹,

Humbert²

2017

IEEE Secur. Privacy

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Genomic data poses serious interdependent risks: your data might also leak information about your family members' data. Methods attackers use to infer genomic information, as well as recent proposals for enhancing genomic privacy, are discussed. Individuals desiring to control their personal data face significant interdependent privacy risks-risks that involve the leakage of one's personal data due to data shared by other individuals. With recent advances in whole genome sequencing, genomic data in particular poses serious interdependent privacy risks.Genomic data has many unique characteristics: it is highly valuable, is an individual's distinctive fingerprint, rarely changes throughout an individual's lifetime, is nonrevocable, and includes sensitive information about an individual (such as disease status or physical characteristics). 1,2 But, the main reason genomic data poses interdependent privacy risks is that it's correlated within family members. Thus, one person's genome-related data (for instance, raw genome, variant call format file, genomic test results, or aggregate statistics) might leak information about the genome-related data of his or her family members.This issue goes all the way back to the DNA dragnets that first raised serious concerns among privacy advocates. Here, we present recent developments on the information security front, including ■ how attackers can infer an individual's genomic data from the partial genomes of his or her family members, background knowledge about genomics (simple statistics, high-order correlations, and so on), and the individual's phenotypic information; ■ how attackers can determine an individual's membership in a particular genomic dataset (for example, a beacon) from only the results of basic queries to that dataset and partial genomic knowledge about the individual's family members; ■ how attackers can deanonymize the deidentified genomes in a public dataset by using the kinship information; and ■ how attackers can efficiently infer kinship from public anonymous genomic databases.

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“…In order to fully utilize the outcomes, homomorphic encryption [8], Yaos Garbled circuits (GC) [19] and reuse of encrypted values [18] have been adapted for data encryption recently. [25][11] [3] Homomorphic encryption is a form of encryption that allows computations to be carried out on ciphertexts.…”

Section: Related Workmentioning

confidence: 99%

Face Search in Encrypted Domain

Yan

Kankanhalli

2016

Image and Video Technology

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Abstract. Visual information of images and videos usually is encrypted for the purposes of security applications. Straightforward manipulations on the encrypted data without requiring any decryption have the advantage of speed over performing those operations in spatial, temporal, frequency or compressed domain. In this paper, we will investigate encrypted image search. More specifically, given a face image as the target object, we search it amongst encrypted images. We accomplish the search by using a novel method that extracts features and locates the face object region within the given encrypted image. We evaluate the search results by using precision and recall as well as F-measure. Our experiments reveal that there exists a trade-off between the quality of search and the quality of encryption, namely, stronger encryption leads to poorer search results.

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Controlled Functional Encryption

Cited by 46 publications

References 46 publications

One-Time Programs Made Practical

One-Time Programs Made Practical

Inference Attacks against Kin Genomic Privacy

Face Search in Encrypted Domain

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