scholarly journals Controlled Functional Encryption Revisited: Multi-Authority Extensions and Efficient Schemes for Quadratic Functions

2021 ◽  
Vol 2021 (1) ◽  
pp. 21-42
Author(s):  
Miguel Ambrona ◽  
Dario Fiore ◽  
Claudio Soriente
Keyword(s):  
Homomorphic Encryption ◽  
Linear Functions ◽  
Quadratic Functions ◽  
Functional Encryption ◽  
Fine Grained ◽  
Multiple Data ◽  
Garbled Circuits ◽  
Private Data ◽  
Data Owner ◽  
Single Data

AbstractIn a Functional Encryption scheme (FE), a trusted authority enables designated parties to compute specific functions over encrypted data. As such, FE promises to break the tension between industrial interest in the potential of data mining and user concerns around the use of private data. FE allows the authority to decide who can compute and what can be computed, but it does not allow the authority to control which ciphertexts can be mined. This issue was recently addressed by Naveed et al., that introduced so-called Controlled Functional encryption (or C-FE), a cryptographic framework that extends FE and allows the authority to exert fine-grained control on the ciphertexts being mined. In this work we extend C-FE in several directions. First, we distribute the role of (and the trust in) the authority across several parties by defining multi-authority C-FE (or mCFE). Next, we provide an efficient instantiation that enables computation of quadratic functions on inputs provided by multiple data-owners, whereas previous work only provides an instantiation for linear functions over data supplied by a single data-owner and resorts to garbled circuits for more complex functions. Our scheme leverages CCA2 encryption and linearly-homomorphic encryption. We also implement a prototype and use it to showcase the potential of our instantiation.

scholarly journals Functional Encryption for Pattern Matching with a Hidden String

Cryptography ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. 1
Author(s):  
Jongkil Kim ◽  
Yang-Wai Chow ◽  
Willy Susilo ◽  
Joonsang Baek ◽  
Intae Kim
Keyword(s):  
Access Control ◽  
Pattern Matching ◽  
Public Information ◽  
Functional Encryption ◽  
Fine Grained ◽  
Private Key ◽  
Diffie Hellman ◽  
Data Owner ◽  
Security Properties

We propose a new functional encryption for pattern matching scheme with a hidden string. In functional encryption for pattern matching (FEPM), access to a message is controlled by its description and a private key that is used to evaluate the description for decryption. In particular, the description with which the ciphertext is associated is an arbitrary string w and the ciphertext can only be decrypted if its description matches the predicate of a private key which is also a string. Therefore, it provides fine-grained access control through pattern matching alone. Unlike related schemes in the literature, our scheme hides the description that the ciphertext is associated with. In many practical scenarios, the description of the ciphertext cannot be public information as an attacker may abuse the message description to identify the data owner or classify the target ciphertext before decrypting it. Moreover, some data owners may not agree to reveal any ciphertext information since it simply gives greater advantage to the adversary. In this paper, we introduce the first FEPM scheme with a hidden string, such that the adversary cannot get any information about the ciphertext from its description. The security of our scheme is formally analyzed. The proposed scheme provides both confidentiality and anonymity while maintaining its expressiveness. We prove these security properties under the interactive general Diffie–Hellman assumption (i-GDH) and a static assumption introduced in this paper.

scholarly journals Techniques and Challenges while Applying Machine Learning Algorithms in Privacy Preserving Fashion

2020 ◽  
Vol 3 ◽  
pp. xix-xix
Author(s):  
Artrim Kjamilji
Keyword(s):  
Machine Learning ◽  
Private Information ◽  
Cyber Security ◽  
Credit Card ◽  
Differential Privacy ◽  
Homomorphic Encryption ◽  
Privacy Preserving ◽  
Machine Learning Algorithms ◽  
Garbled Circuits ◽  
Private Data

Nowadays many different entities collect data of the same nature, but in slightly different environments. In this sense different hospitals collect data about their patients’ symptoms and corresponding disease diagnoses, different banks collect transactions of their customers’ bank accounts, multiple cyber-security companies collect data about log files and corresponding attacks, etc. It is shown that if those different entities would merge their privately collected data in a single dataset and use it to train a machine learning (ML) model, they often end up with a trained model that outperforms the human experts of the corresponding fields in terms of accurate predictions. However, there is a drawback. Due to privacy concerns, empowered by laws and ethical reasons, no entity is willing to share with others their privately collected data. The same problem appears during the classification case over an already trained ML model. On one hand, a user that has an unclassified query (record), doesn’t want to share with the server that owns the trained model neither the content of the query (which might contain private data such as credit card number, IP address, etc.), nor the final prediction (classification) of the query. On the other hand, the owner of the trained model doesn’t want to leak any parameter of the trained model to the user. In order to overcome those shortcomings, several cryptographic and probabilistic techniques have been proposed during the last few years to enable both privacy preserving training and privacy preserving classification schemes. Some of them include anonymization and k-anonymity, differential privacy, secure multiparty computation (MPC), federated learning, Private Information Retrieval (PIR), Oblivious Transfer (OT), garbled circuits and/or homomorphic encryption, to name a few. Theoretical analyses and experimental results show that the current privacy preserving schemes are suitable for real-case deployment, while the accuracy of most of them differ little or not at all with the schemes that work in non-privacy preserving fashion.

scholarly journals Securely Outsourcing ID3 Decision Tree in Cloud Computing

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Ye Li ◽  
Zoe L. Jiang ◽  
Xuan Wang ◽  
Junbin Fang ◽  
En Zhang ◽  
...  
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Decision Tree ◽  
Homomorphic Encryption ◽  
Privacy Preserving ◽  
Security Model ◽  
Garbled Circuits ◽  
Private Data ◽  
Malicious Model ◽  
And Performance

With the wide application of Internet of Things (IoT), a huge number of data are collected from IoT networks and are required to be processed, such as data mining. Although it is popular to outsource storage and computation to cloud, it may invade privacy of participants’ information. Cryptography-based privacy-preserving data mining has been proposed to protect the privacy of participating parties’ data for this process. However, it is still an open problem to handle with multiparticipant’s ciphertext computation and analysis. And these algorithms rely on the semihonest security model which requires all parties to follow the protocol rules. In this paper, we address the challenge of outsourcing ID3 decision tree algorithm in the malicious model. Particularly, to securely store and compute private data, the two-participant symmetric homomorphic encryption supporting addition and multiplication is proposed. To keep from malicious behaviors of cloud computing server, the secure garbled circuits are adopted to propose the privacy-preserving weight average protocol. Security and performance are analyzed.

scholarly journals Efficient Private Conjunctive Query Protocol Over Encrypted Data

Cryptography ◽  
2021 ◽  
Vol 5 (1) ◽  
pp. 2
Author(s):  
Tushar Kanti Saha ◽  
Takeshi Koshiba
Keyword(s):  
Homomorphic Encryption ◽  
Data Access ◽  
Conjunctive Query ◽  
Conjunctive Queries ◽  
Private Data ◽  
Binary Format ◽  
Main Technique ◽  
Batch Technique ◽  
Outsourced Database ◽  
Packing Method

Conjunctive queries play a key role in retrieving data from a database. In a database, a query containing many conditions in its predicate, connected by an “and/&/∧” operator, is called a conjunctive query. Retrieving the outcome of a conjunctive query from thousands of records is a heavy computational task. Private data access to an outsourced database is required to keep the database secure from adversaries; thus, private conjunctive queries (PCQs) are indispensable. Cheon, Kim, and Kim (CKK) proposed a PCQ protocol using search-and-compute circuits in which they used somewhat homomorphic encryption (SwHE) for their protocol security. As their protocol is far from being able to be used practically, we propose a practical batch private conjunctive query (BPCQ) protocol by applying a batch technique for processing conjunctive queries over an outsourced database, in which both database and queries are encoded in binary format. As a main technique in our protocol, we develop a new data-packing method to pack many data into a single polynomial with the batch technique. We further enhance the performances of the binary-encoded BPCQ protocol by replacing the binary encoding with N-ary encoding. Finally, we compare the performance to assess the results obtained by the binary-encoded BPCQ protocol and the N-ary-encoded BPCQ protocol.

scholarly journals Combining disparate data sources for improved poverty prediction and mapping

2017 ◽  
Vol 114 (46) ◽  
pp. E9783-E9792 ◽  
Author(s):  
Neeti Pokhriyal ◽  
Damien Christophe Jacques
Keyword(s):  
Bayesian Learning ◽  
Pearson Correlation ◽  
Gaussian Process Regression ◽  
Multidimensional Poverty ◽  
Environmental Data ◽  
Policy Makers ◽  
Multidimensional Poverty Index ◽  
Fine Grained ◽  
Disparate Data ◽  
Single Data

More than 330 million people are still living in extreme poverty in Africa. Timely, accurate, and spatially fine-grained baseline data are essential to determining policy in favor of reducing poverty. The potential of “Big Data” to estimate socioeconomic factors in Africa has been proven. However, most current studies are limited to using a single data source. We propose a computational framework to accurately predict the Global Multidimensional Poverty Index (MPI) at a finest spatial granularity and coverage of 552 communes in Senegal using environmental data (related to food security, economic activity, and accessibility to facilities) and call data records (capturing individualistic, spatial, and temporal aspects of people). Our framework is based on Gaussian Process regression, a Bayesian learning technique, providing uncertainty associated with predictions. We perform model selection using elastic net regularization to prevent overfitting. Our results empirically prove the superior accuracy when using disparate data (Pearson correlation of 0.91). Our approach is used to accurately predict important dimensions of poverty: health, education, and standard of living (Pearson correlation of 0.84–0.86). All predictions are validated using deprivations calculated from census. Our approach can be used to generate poverty maps frequently, and its diagnostic nature is, likely, to assist policy makers in designing better interventions for poverty eradication.

scholarly journals Effects of transient temperature on human's heat losses

2016 ◽  
Vol 20 (3) ◽  
pp. 827-830
Author(s):  
Lijuan Wang ◽  
Yuhui Di ◽  
Hui Yin ◽  
Yanfeng Liu ◽  
Jiaping Liu
Keyword(s):  
Energy Balance ◽  
Air Conditioning ◽  
Heat Losses ◽  
Linear Functions ◽  
Quadratic Functions ◽  
Balance Model ◽  
Transient Temperature ◽  
Convection Diffusion ◽  
Comfort Evaluation ◽  
And Diffusion

The objectives of the paper are to analyze human convection, radiation, evaporation, respiration, conduction, and diffusion heat losses when the operative temperature increases from 26-34.4?C and then decreases from 34.4-26?C with a ratio of 1.4?C per 5 minutes. A energy balance model is used for sedentary subject. The results show that during temperature rising, all the heat losses are linear functions of temperature, while during temperature dropping, the convection, diffusion, and respiration heat losses are quadratic functions of temperature. The results are useful for thermal comfort evaluation and heating, ventilation, and air conditioning design.

Multi-Keyword Searchable Encryption for E-Health System With Multiple Data Writers and Readers

2022 ◽  
pp. 107-131
Author(s):  
Dhruti P. Sharma ◽  
Devesh C. Jinwala
Keyword(s):  
Health System ◽  
Keyword Search ◽  
Medical Data ◽  
Searchable Encryption ◽  
Multiple Stakeholders ◽  
Computational Overhead ◽  
Multiple Data ◽  
Data Owner ◽  
Share Data ◽  
Conjunctive Search

E-health is a cloud-based system to store and share medical data with the stakeholders. From a security perspective, the stored data are in encrypted form that could further be searched by the stakeholders through searchable encryption (SE). Practically, an e-health system with support of multiple stakeholders (that may work as either data owner [writer] or user [reader]) along with the provision of multi-keyword search is desirable. However, the existing SE schemes either support multi-keyword search in multi-reader setting or offer multi-writer, multi-reader mechanism along with single-keyword search only. This chapter proposes a multi-keyword SE for an e-health system in multi-writer multi-reader setting. With this scheme, any registered writer could share data with any registered reader with optimal storage-computational overhead on writer. The proposed scheme offers conjunctive search with optimal search complexity at server. It also ensures security to medical records and privacy of keywords. The theoretical and empirical analysis demonstrates the effectiveness of the proposed work.

scholarly journals Traceable Attribute-Based Secure Data Sharing with Hidden Policies in Mobile Health Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Xueyan Liu ◽  
Yukun Luo ◽  
Xiaotao Yang
Keyword(s):  
Bilinear Pairing ◽  
Health Networks ◽  
Access Policy ◽  
Fine Grained ◽  
Attribute Based Encryption ◽  
Data Owner ◽  
Data User ◽  
Efficient Scheme ◽  
And Performance ◽  
The One

The growing need to store, share, and manage medical and health records has resulted in electronic medical health sharing system (mHealth), which provides intelligent medical treatment for people. Attribute-based encryption (ABE) is regarded as a new cryptology to enhance fine-grained access control over encrypted sharing data in mHealth. However, some existing attribute-based mHealth systems not only violate the one-to-many application characteristics of attribute-based encryption mechanism but also destroy the anonymity of user. In this study, an efficient scheme is proposed to tackle the above defaults and offer two-way anonymity of data owner and data user by introducing a pseudoidentity. The computation of hidden access policy is reduced by removing the bilinear pairing, whereas the interaction between cloud storage and data user is avoided to save bandwidth during trapdoor generation. We also consider the temporal factor of the uploaded information by introducing access validity. Security and performance analyses show that the proposed scheme is efficient without reducing security.

scholarly journals Credibility Approximations for Bayesian Prediction of Second Moments

1985 ◽  
Vol 15 (2) ◽  
pp. 103-121 ◽  
Author(s):  
William S. Jewell ◽  
Rene Schnieper
Keyword(s):  
Least Squares ◽  
Linear Functions ◽  
Quadratic Functions ◽  
Sample Variance ◽  
Credibility Theory ◽  
Sample Mean ◽  
Second Moment ◽  
Second Moments ◽  
Special Cases ◽  
The Mean

AbstractCredibility theory refers to the use of linear least-squares theory to approximate the Bayesian forecast of the mean of a future observation; families are known where the credibility formula is exact Bayesian. Second-moment forecasts are also of interest, for example, in assessing the precision of the mean estimate. For some of these same families, the second-moment forecast is exact in linear and quadratic functions of the sample mean. On the other hand, for the normal distribution with normal-gamma prior on the mean and variance, the exact forecast of the variance is a linear function of the sample variance and the squared deviation of the sample mean from the prior mean. Bühlmann has given a credibility approximation to the variance in terms of the sample mean and sample variance.In this paper, we present a unified approach to estimating both first and second moments of future observations using linear functions of the sample mean and two sample second moments; the resulting least-squares analysis requires the solution of a 3 × 3 linear system, using 11 prior moments from the collective and giving joint predictions of all moments of interest. Previously developed special cases follow immediately. For many analytic models of interest, 3-dimensional joint prediction is significantly better than independent forecasts using the “natural” statistics for each moment when the number of samples is small. However, the expected squared-errors of the forecasts become comparable as the sample size increases.

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