The collection and analysis of data are continuously growing due to the pervasiveness of computing devices. The analysis of such information is fostering businesses and contributing beneficially to the society in many different fields. However, this storage and flow of possibly sensitive data poses serious privacy concerns. Methods that allow the knowledge extraction from data, while preserving privacy, are known as privacy-preserving data mining (PPDM) techniques. This paper surveys the most relevant PPDM techniques from the literature and the metrics used to evaluate such techniques and presents typical applications of PPDM methods in relevant fields. Furthermore, the current challenges and open issues in PPDM are discussed.

Privacy-Preserving Data Mining: Methods, Metrics, and Applications

Big data require cloud that provides dynamically expanding data storage accessed through the Internet. The outsourcing data in the cloud for storing makes the user data management easier and reduces the cost of maintaining data. Still organizations are not confident to store their data in the cloud, because of security and privacy concerns. However, existing encryption methods are able to protect data confidentiality, but it has some drawbacks of access patterns can also leak sensitive information. The proposed system uses an Elliptic curve with Diffie–Hellman (ECDH) algorithm for encryption and decryption of data to improve the data security in the cloud. This algorithm reduced the computational complexity and encrypted data efficiently. In experimental analysis, the performance of proposed ECDH is calculated using evaluation parameters such as encryption time, decryption time, computation overhead and key generation time. The proposed ECDH algorithm has approximately 70% better performance in terms of encryption time than existing methods such as RSA, MRSA and MRSAC.

Elliptic curve Diffie–Hellman cryptosystem in big data cloud security

Enabling a security and privacy preservation for the cloud data is one of the demanding and crucial tasks in recent days. Because, the privacy of the sensitive data should be safeguard from the unauthorized access for improving its security. So, various key generation, encryption and decryption mechanisms are developed in the traditional works for privacy preservation in cloud. Still, it remains with the issues such as increased computational complexity, time consumption, and reduced security. Also, the traditional works use the symmetric key cryptography based. Thus, this paper aims to develop a new privacy preservation mechanism by implementing a fully homomorphic–elliptic curve cryptography (FH-ECC) algorithm. The data owner encrypts the original data by converting it into the cipher format with the use of ECC algorithm, and applies the FH operations on the encrypted data before storing it on the cloud. When the user gives the data request to the cloud, the Cloud Service Provider verifies the access control policy of the user for enabling the restricted access on the data. If the access policy is verified, the encrypted data is provided to the user, from that the cipher text is extracted. Then, the ECC decryption and FH operations are applied to generate the original text. Based on the several analysis, the research work is evaluated with the help of different performance measures such as execution time, encryption time, and decryption time. In addition the effectiveness of the novel FHE technique is justified by the comparative analysis made with the traditional techniques.

A fully homomorphic–elliptic curve cryptography based encryption algorithm for ensuring the privacy preservation of the cloud data

K-anonymity is an effective model for protecting privacy while publishing data, which can be implemented by different ways. Among them, local generalization are popular because of its low information loss. But such algorithms are generally computation expensive making it difficult to perform well in the case of large amount of data. In order to solve this problem, this paper proposes a clustering based K-anonymity algorithm and optimizes it with parallelization. The experimental result shows that the algorithm performs better in information loss and performance compared with the existing KACA and Incognito algorithms.

/pdf/clustering-based-k-anonymity-algorithm-for-privacy-3i6ck1skjj.pdf

Clustering Based K-anonymity Algorithm for Privacy Preservation.

In order to make technological systems and platforms more equitable, organizations must be able to measure the scale of potential inequities as well as the efficacy of proposed solutions. In this paper, we present a system that measures discrepancies in platform user experience that are attributable to perceived race (experience gaps) using anonymized data. This allows for progress to be made in this area while limiting any potential privacy risk. Specifically, the system enforces the privacy model of p-sensitive k-anonymity to conduct measurement without ever storing or having access to a 1:1 mapping between user identifiers and perceived race. We test this system in the context of the Airbnb guest booking experience. Our simulation-based power analysis shows that the system can measure the efficacy of proposed platform-wide interventions with comparable precision to non-anonymized data. Our work establishes that measurement of experience gaps with anonymized data is feasible and can be used to guide the development of policies to promote equitable outcomes for users of Airbnb as well as other technology platforms.

Measuring Discrepancies in Airbnb Guest Acceptance Rates Using Anonymized Demographic Data

Data security is one of the leading concerns and primary challenges for cloud computing. This issue is getting more and more serious with the development of cloud computing. However, the existing privacy-preserving data sharing techniques either fail to prevent the leakage of privacy or incur huge amounts of information loss. In this paper, we propose a novel technique, termed as linking-based anonymity model, which achieves K-anonymity with quasi-identifiers groups (QI-groups) having a size less than K. In the meanwhile, a semi-homogenous generalization is introduced to be against the attack incurred by homogenous generalization. To implement linking-based anonymization model, we propose a simple yet efficient heuristic local recoding method. Extensive experiments on real datasets are also conducted to show that the utility has been significantly improved by our approach compared with the state-of-the-art methods.

Semi-Homogenous Generalization: Improving Homogenous Generalization for Privacy Preservation in Cloud Computing

Klapper (1994) showed that there exists a class of geometric sequences with the maximal possible linear complexity when considered as sequences over GF(2), but these sequences have very low linear complexities when considered as sequences over GF(p) (p is an odd prime). This linear complexity of a binary sequence when considered as a sequence over GF(p) is called GF(p) complexity. This indicates that the binary sequences with high GF(2) linear complexities are inadequate for security in the practical application, while, their GF(p) linear complexities are also equally important, even when the only concern is with attacks using the Berlekamp-Massey algorithm [Massey, J. L., Shift-register synthesis and bch decoding, IEEE Transactions on Information Theory, 15(1), 1969, 122–127]. From this perspective, in this paper the authors study the GF(p) linear complexity of Hall’s sextic residue sequences and some known cyclotomic-set-based sequences.

On the GF(p) linear complexity of Hall’s sextic sequences and some cyclotomic-set-based sequences

In this paper, we focus on the construction methods based MWD for polar codes to improve the performance with successive cancellation list (SCL) decoding. We first propose an ordered and nested reliability sequence, namely MWD sequence, to improve the ML performance of polar codes and apply fast construction without the original channel information. In the MWD sequence, the synthetic channels are sorted by the partial MWD which is used to evaluate the influence of information bit on MWD and we prove the MWD sequence is the optimum sequence under ML decoding. Then, since the list size of SCL decoding is limited, we introduce an entropy constraint to establish a relationship between the list size and the ML performance and propose a heuristic and greedy construction method named bit grouping reorder based MWD (BGR-MWD) algorithm. In the algorithm, we divide the synthetic channels into groups by the partial MWD and greedily reorder the synthetic channels in some groups until the entropy constraint is satisfied. The simulation results show the MWD sequence is suitable for constructing polar codes with short code length. Meanwhile, the BGR-MWD algorithm has superior performance over the traditional construction methods for long code length.

pdf/construction-methods-based-minimum-weight-distribution-for-2qrwgnop.pdf

Construction Methods Based Minimum Weight Distribution for Polar Codes with Successive Cancellation List Decoding

In this paper, we first indicate that the block error event of polar codes under successive cancellation list (SCL) decoding is composed of path loss (PL) error event and path selection (PS) error event, where the PL error event is that correct codeword is lost during the SCL decoding and the PS error event is that correct codeword is reserved in the decoded list but not selected as the decoded codeword. Then, we simplify the PL error event by assuming the all-zero codeword is transmitted and derive the probability lower bound via the joint probability density of the log-likelihood ratios of information bits. Meanwhile, the union bound calculated by the minimum weight distribution is used to evaluate the probability of the PS error event. With the performance analysis, we design a greedy bit-swapping (BS) algorithm to construct polar codes by gradually swapping information bit and frozen bit to reduce the performance lower bound of SCL decoding. The simulation results show that the BLER performance of SCL decoding is close to the lower bound in the medium to high signal-to-noise ratio region and we can optimize the lower bound to improve the BLER performance of SCL decoding by the BS algorithm.

Performance Analysis for Polar Codes under Successive Cancellation List Decoding with Fixed List Size

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Papers

Semi-Homogenous Generalization: Improving Homogenous Generalization for Privacy Preservation in Cloud Computing

On the GF(p) linear complexity of Hall’s sextic sequences and some cyclotomic-set-based sequences

Construction Methods Based Minimum Weight Distribution for Polar Codes with Successive Cancellation List Decoding

Performance Analysis for Polar Codes under Successive Cancellation List Decoding with Fixed List Size