scholarly journals Frequency-smoothing encryption: preventing snapshot attacks on deterministically encrypted data

2018 ◽  
pp. 277-313 ◽  
Author(s):  
Marie-Sarah Lacharité ◽  
Kenneth G. Paterson
Keyword(s):  
Statistical Analysis ◽  
Frequency Analysis ◽  
Real Data ◽  
Searchable Encryption ◽  
Encrypted Data ◽  
Attack Model ◽  
Deterministic Encryption ◽  
Classical Technique ◽  
Inference Attacks ◽  
Provably Secure

Statistical analysis of ciphertexts has been recently used to carry out devastating inference attacks on deterministic encryption (Naveed, Kamara, and Wright, CCS 2015), order-preserving/revealing encryption (Grubbs et al., S&P 2017), and searchable encryption (Pouliot and Wright, CCS 2016). At the heart of these inference attacks is classical frequency analysis. In this paper, we propose and evaluate another classical technique, homophonic encoding, as a means to combat these attacks. We introduce and develop the concept of frequency-smoothing encryption (FSE) which provably prevents inference attacks in the snapshot attack model, wherein the adversary obtains a static snapshot of the encrypted data, while preserving the ability to efficiently and privately make point queries. We provide provably secure constructions for FSE schemes, and we empirically assess their security for concrete parameters by evaluating them against real data. We show that frequency analysis attacks (and optimal generalisations of them for the FSE setting) no longer succeed.

The Effects of Exercise Commitment Facilitated by Service Quality of Smartphone Exercise Applications on Continued Exercise Intention

2019 ◽  
Vol 118 (1) ◽  
pp. 14-19
Author(s):  
Boo-Gil Seok ◽  
Hyun-Suk Park
Keyword(s):  
College Students ◽  
Factor Analysis ◽  
Regression Analysis ◽  
Statistical Analysis ◽  
Correlation Analysis ◽  
Service Quality ◽  
Frequency Analysis ◽  
Primary Data ◽  
Exercise Intention

Background/Objectives: The purpose of this study is to examine the effects of exercise commitment facilitated by service quality of smartphone exercise Apps on continued exercise intention and provide primary data for developing and/or improving smartphone exercise Apps. Methods/Statistical analysis: A questionnaire survey was conducted amongst college students who have experiences in using exercise App(s) and regularly exercise. The questionnaire is composed of four parts asking about service quality, exercise commitment, continued exercise intention, which were measured with a 5-point Likert Scale, and demographics. Frequency analysis, factor analysis, correlation analysis, and regression analysis were carried out to analyze the obtained data with PASW 18.0.

Structural Relationships among Customer Delight, Exercise Commitment, and Psychological Happiness

2019 ◽  
Vol 118 (1) ◽  
pp. 8-13
Author(s):  
Boo-Gil Seok ◽  
Hyun-Suk Park
Keyword(s):  
College Students ◽  
Factor Analysis ◽  
Statistical Analysis ◽  
Confirmatory Factor Analysis ◽  
Correlation Analysis ◽  
Frequency Analysis ◽  
Structural Correlation ◽  
Structural Relationships ◽  
Confirmatory Factor ◽  
Customer Delight

Background/Objectives: The purpose of this study is to find out the structural relationships among customer delight, exercise commitment, and psychological happiness to contribute developing exercise Apps. Methods/Statistical analysis: A questionnaire survey was conducted and 160 college students who are familiar with mobile exercise applications participated. The data analyzed with frequency analysis, exploratory factor analysis, confirmatory factor analysis, correlation analysis, and structural correlation analysis. The validity and the reliability were obtained: customer delight (χ2=26.532, df=14, CFI=.985, TLI=.971, RMSEA=.075), exercise commitment (χ2=113.802, df=49, CFI=.956, TLI=.941, RMSEA=.091), and psychological happiness (χ2=15.338, df=8, CFI=.989, TLI=.980, RMSEA=.076, and Cronbach’s α=.906~.938).

scholarly journals Leakage Models and Inference Attacks on Searchable Encryption for Cyber-Physical Social Systems

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 21828-21839 ◽  
Author(s):  
Guofeng Wang ◽  
Chuanyi Liu ◽  
Yingfei Dong ◽  
Kim-Kwang Raymond Choo ◽  
Peiyi Han ◽  
...  
Keyword(s):  
Social Systems ◽  
Searchable Encryption ◽  
Inference Attacks

scholarly journals EFFICIENT AND EXPRESSIVE KEYWORD SEARCH OVER ENCRYPTED DATA IN CLOUD

2020 ◽  
pp. 145-147
Author(s):  
Zeeshan Sharief
Keyword(s):  
Keyword Search ◽  
Bilinear Pairings ◽  
Searchable Encryption ◽  
Encrypted Data ◽  
Access Structures ◽  
Attribute Based Encryption ◽  
Cloud Server ◽  
Encryption Schemes ◽  
Boolean Formulas ◽  
Significant Performance

Searchable encryption allows a cloud server to conduct keyword search over encrypted data on behalf of the data users without learning the underlying plaintexts. However, most existing searchable encryption schemes only support single or conjunctive keyword search, while a few other schemes that can perform expressive keyword search are computationally inefficient since they are built from bilinear pairings over the composite-order groups. In this paper, we propose an expressive public-key searchable encryption scheme in the prime-order groups, which allows keyword search policies i.e., predicates, access structures to be expressed in conjunctive, disjunctive or any monotonic Boolean formulas and achieves significant performance improvement over existing schemes. We formally define its security and prove that it is selectively secure in the standard model. Also, we implement the proposed scheme using a rapid prototyping tool called Charm and conduct several experiments to evaluate it performance. The results demonstrate that our scheme is much more efficient than the ones built over the composite-order groups. INDEX TERMS - Searchable encryption, cloud computing, expressiveness, attribute-based encryption

Aggregate Searchable Encryption With Result Privacy

2021 ◽  
pp. 922-945
Author(s):  
Dhruti P. Sharma ◽  
Devesh C. Jinwala
Keyword(s):  
Data Analysis ◽  
Count Data ◽  
Data Extraction ◽  
Searchable Encryption ◽  
Primary Concern ◽  
Conjunctive Query ◽  
Encrypted Data ◽  
Storage Server ◽  
Partial Data ◽  
Aggregate Query

With searchable encryption (SE), the user is allowed to extract partial data from stored ciphertexts from the storage server, based on a chosen query of keywords. A majority of the existing SE schemes support SQL search query, i.e. 'Select * where (list of keywords).' However, applications for encrypted data analysis often need to count data matched with a query, instead of data extraction. For such applications, the execution of SQL aggregate query, i.e. 'Count * where (list of keywords)' at server is essential. Additionally, in case of semi-honest server, privacy of aggregate result is of primary concern. In this article, the authors propose an aggregate searchable encryption with result privacy (ASE-RP) that includes ASearch() algorithm. The proposed ASearch() performs aggregate operation (i.e. Count *) on the implicitly searched ciphertexts (for the conjunctive query) and outputs an encrypted result. The server, due to encrypted form of aggregate result, would not be able to get actual count unless having a decryption key and hence ASearch() offers result privacy.

Emerging Technologies in a Modern Competitive Scenario

2021 ◽  
pp. 1-16
Author(s):  
George Leal Jamil ◽  
Alexis Rocha da Silva
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Statistical Analysis ◽  
Differential Privacy ◽  
Social Issues ◽  
Training Data ◽  
Sensitive Data ◽  
Machine Learning Model ◽  
Highly Sensitive ◽  
Inference Attacks

Users' personal, highly sensitive data such as photos and voice recordings are kept indefinitely by the companies that collect it. Users can neither delete nor restrict the purposes for which it is used. Learning how to machine learning that protects privacy, we can make a huge difference in solving many social issues like curing disease, etc. Deep neural networks are susceptible to various inference attacks as they remember information about their training data. In this chapter, the authors introduce differential privacy, which ensures that different kinds of statistical analysis don't compromise privacy and federated learning, training a machine learning model on a data to which we do not have access to.

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