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With the advent and development of database applications such as big data and data mining, how to ensure the availability of data without revealing sensitive information has been a significant problem for database privacy protection. As a critical technology to solve this problem, homomorphic encryption has become a hot research area in information security at home and abroad in recent years. The paper sorted out, analyzed, and summarized the research progress of homomorphic encryption technology in database privacy protection. Moreover, the application of three different types of homomorphic encryption technology in database privacy protection was introduced respectively, and the rationale and characteristics of each technique were analyzed and explained. Ultimately, this research summarized the challenges and development trends of homomorphic encryption technology in the application of database privacy protection, which provides a reference for future research.

Evolution and modernization have brought about progress in technology and this has led to the reduction in privacy & internet security due to an increase in cybercrime and threats. As a result of this turn of events, Cryptography is now being used as a means of keeping information of any kind safe from third party individual(s). Research has shown that with the Encryption of information, third party individual(s) have no chance or less chance of getting past this security measure. Hence, Cryptographers keep improving algorithms to make it impossible for a third party to decrypt this information without the key which is where database Privacy and Security come in. The database contains all the information which is a major asset, there are encryptions which can be used at different levels to provide security. Lastly, for encryption algorithms which are breached by unknown third-party individual(s), the zero knowledge of proof helps to figure out the identity of this individual. They are an extremely interesting and useful construct. They are fascinating because of their definition, which is mutually opposed, their applicability is very vast in cryptography; they are used to restrict the malevolent users to work according to the protocol. Zero-knowledge serve as a good medium to understand the problems regarding cryptographic protocols. Keywords: Cipher, Encryption, Decryption, Key, Security, Database, Zero-Knowledge

Recently, Gao et al. suggested a flexible quantum private query (QPQ) protocol (Opt Express, 2012, 20(16):17411) through Jakobi et al.’s quantum-key-distribution based QPQ protocol (Phys Rev A, 2011, 83:022301). Inspired by Gao et al.’s QPQ protocol, we design a multi-user QPQ protocol by using the product states of single-photon states. Its database privacy and user privacy should be compromised by choosing an suitable value for θ. Due to the honesty check process for the database owner, an smaller θ can be priorly chosen to guarantee better database privacy. Moreover, it naturally inherits some good characteristics of Gao et al.’s QPQ protocol so that it is also loss-tolerant, practical to huge-database condition, robust against quantum memory attack and flexible. It differs from Gao et al.’s QPQ protocol on three aspects: firstly, it adds the process of checking the presence of an illegal eavesdropper; secondly, it adds the process of checking the database owner’s honesty; and finally, it is feasible for n users to query the items of database simultaneously.

Differential privacy provides strong privacy guarantees simultaneously enabling useful insights from sensitive datasets. However, it provides the same level of protection for all elements (individuals and attributes) in the data. There are practical scenarios where some data attributes need more/less protection than others. In this paper, we consider dX -privacy, an instantiation of the privacy notion introduced in [6], which allows this flexibility by specifying a separate privacy budget for each pair of elements in the data domain. We describe a systematic procedure to tailor any existing differentially private mechanism that assumes a query set and a sensitivity vector as input into its d X -private variant, specifically focusing on linear queries. Our proposed meta procedure has broad applications as linear queries form the basis of a range of data analysis and machine learning algorithms, and the ability to define a more flexible privacy budget across the data domain results in improved privacy/utility tradeoff in these applications. We propose several d X -private mechanisms, and provide theoretical guarantees on the trade-off between utility and privacy. We also experimentally demonstrate the effectiveness of our procedure, by evaluating our proposed d X -private Laplace mechanism on both synthetic and real datasets using a set of randomly generated linear queries.

The quantum-key-distribution (QKD)-based quantum private query (QPQ) has become a research hotspot in recent years. Although such QPQ protocols are practical, joint-measurement (JM) attack is a noteworthy threat to the security of the database. In this paper, we propose a new QPQ protocol for enhancing database security against JM attack. The special procedure “receive→measure→re-prepare→send” for the user Alice prevents her from saving states to perform JM attack. Furthermore, since each photon only travels from one party to the other, our protocol reduces the drawback of two-way communication so that the transmission distance between Alice and the database holder Bob is satisfactory. We also analyze the security of the proposed protocol in terms of the database privacy and user privacy. Moreover, our protocol preserves the excellent character of QKD-based QPQs that it is loss tolerant.

Continuing with the goal of limiting disclosure, this chapter examines technologies that hide the attribute information of the user. The following example PETs in this category are described: ciphertext-policy attribute-based encryption; multi-party computation; and e-differential privacy. For each presented PET, the basic scheme, enhancements, strengths, and limitations are described.

The pervasiveness of camera technology in every-day life begets a modern reality in which images of individuals are routinely captured on a daily basis. Although this has enabled many benefits, it also infringes on personal privacy. To mitigate the loss of privacy, researchers have investigated methods of facial obfuscation in images. A promising direction has been the work in the k-same family of methods which employ the concept of k-anonymity from database privacy. However, there are a number of deficiencies of k-anonymity which carry over to the k-same methods, detracting from their usefulness in practice. In this paper, we first outline several of these deficiencies and discuss their implications in the context of facial obfuscation. We then develop the first framework to apply the formal privacy guarantee of differential privacy to facial obfuscation in generative machine learning models for images. Next, we discuss the theoretical improvements in the privacy guarantee which make this approach more appropriate for practical usage. Our approach provides a provable privacy guarantee which is not susceptible to the outlined deficiencies of k-same obfuscation and produces photo-realistic obfuscated output. Finally, while our approach provides a stronger privacy guarantee, we demonstrate through experimental comparisons that it can achieve comparable utility to k-same approaches in the context of preservation of demographic information in the images. The preservation of such information is of particular importance for enabling effective data mining on the obfuscated images.

We propose a novel fast perturbation algorithm for protecting database privacy.We introduce a new tree structure for effectively performing perturbation processes.Traversal techniques using two tables are employed to reduce the search space of PPUM.We demonstrate the efficiency by performing theoretical and empirical comparisons.Experimental results show that FPUTT is 5-10 times faster than previous algorithms. As one of the important approaches in privacy preserving data mining, privacy preserving utility mining has been studied to find more meaningful results while database privacy is ensured and to improve algorithm efficiency by integrating fundamental utility pattern mining and privacy preserving data mining methods. However, its previous approaches require a significant amount of time to protect the privacy of data holders because they conduct database scanning operations excessively many times until all important information is hidden. Moreover, as the size of a given database becomes larger and a user-specified minimum utility threshold becomes lower, their performance degradation may be so uncontrollable that they cannot operate normally. To solve this problem, we propose a fast perturbation algorithm based on a tree structure which more quickly performs database perturbation processes for preventing sensitive information from being exposed. We also present extensive experimental results between our proposed method and state-of-the-art algorithms using both real and synthetic datasets. They show the proposed method has not only outstanding privacy preservation performance that is comparable to the previous ones but also 5-10 times faster runtime than that of the existing approaches on average. In addition, the proposed algorithm guarantees better scalability than that of the latest ones with respect to databases with the characteristics of gradually increasing attributes and transactions.

With the proliferation of mobile devices and the internet of things, developing principled solutions for privacy in time series applications has become increasingly important. While differential privacy is the gold standard for database privacy, many time series applications require a different kind of guarantee, and a number of recent works have used some form of inferential privacy to address these situations. However, a major barrier to using inferential privacy in practice is its lack of graceful composition -- even if the same or related sensitive data is used in multiple releases that are safe individually, the combined release may have poor privacy properties. In this paper, we study composition properties of a form of inferential privacy called Pufferfish when applied to time-series data. We show that while general Pufferfish mechanisms may not compose gracefully, a specific Pufferfish mechanism, called the Markov Quilt Mechanism, which was recently introduced, has strong composition properties comparable to that of pure differential privacy when applied to time series data.

We study the problem of anonymizing data by column suppression. Meyerson and Williams showed that this problem is NP-hard for k ? 3 . The complexity of this problem for k = 2 remained open. In this note, we show that 2-anonymizing data by suppressing the minimum number of columns is also NP-hard. In fact, we prove a stronger claim that this problem is NP-hard to approximate within a factor of ? ( log ? m ) , where m is the number of columns in the table. Furthermore, our proof also shows that this problem, parameterized by the number of columns to be suppressed, is not in FPT unless W 2 = FPT . We study 2-anonymization of tables by suppressing smallest number of attributes.We show that it is unlikely to be FPT when parametrized by number of attributes.Our proof also shows that this problem is NP-hard.

Database is important for any application and critical part of private and public cloud platforms. For compatibility with cloud computing we can follow architectures like three tier architecture in .Net Technologies such that database layer should be separate from user and business logic layers. There are some other issues like following ACID properties in databases, providing dynamic scalability by using Shared-disk Architecture and efficient multi-tenancy, elastic scalability, and database privacy.

In this article, we demonstrate that, ignoring computational constraints, it is possible to release synthetic databases that are useful for accurately answering large classes of queries while preserving differential privacy. Specifically, we give a mechanism that privately releases synthetic data useful for answering a class of queries over a discrete domain with error that grows as a function of the size of the smallest net approximately representing the answers to that class of queries. We show that this in particular implies a mechanism for counting queries that gives error guarantees that grow only with the VC-dimension of the class of queries, which itself grows at most logarithmically with the size of the query class. We also show that it is not possible to release even simple classes of queries (such as intervals and their generalizations) over continuous domains with worst-case utility guarantees while preserving differential privacy. In response to this, we consider a relaxation of the utility guarantee and give a privacy preserving polynomial time algorithm that for any halfspace query will provide an answer that is accurate for some small perturbation of the query. This algorithm does not release synthetic data, but instead another data structure capable of representing an answer for each query. We also give an efficient algorithm for releasing synthetic data for the class of interval queries and axis-aligned rectangles of constant dimension over discrete domains.

One of the most pressing issues in the confidentiality literature is the quantification of privacy. One proposal, $$ \epsilon $$ -differential privacy, moves away from absolute guarantees of privacy to relative guarantees. However, the selection of an appropriate $$ \epsilon $$ is difficult because its interpretation is unclear. Further, when comparing different privacy preserving techniques to one another, a direct comparison cannot be made by simply comparing the respective values of $$ \epsilon $$ . The aim of this work is to provide a measure that allows for direct comparison across different privacy schemes and is more easily interpreted. In turn, this will aid in policy debate pertaining to how much privacy is acceptable. Our proposal sets the problem in a hypothesis testing framework and uses the area under the receiver-operator characteristic (ROC) curve as a measure of privacy.

The emphasis in database privacy should fall on a balance between confidentiality, integrity and availability of personal data, rather than on confidentiality alone. This balance should not necessarily be a trade-off, but should take into account the sensitive nature of the data being stored and attempt to increase all three dimensions to the highest level possible.To achieve such a balance, technological means should be developed.The paper illustrates some of the inherent problems in database privacy that should be addressed by technical solutions. It next demonstrates that the notion of privacy is complex; this complexity is likely to impede development of technical solutions.Finally, the paper finally uses the notion of informed consent to illustrate how the privacy problem can be viewed from multiple angles to flesh out the underlying problems that may be addressed by technical solutions.

Database privacy can be conceptually described in terms of three dimensions, each of which refers to the privacy of a different subject and is pursued by a different discipline: respondent privacy, addressed by statistical disclosure control (SDC), owner privacy, addressed by privacy-preserving data mining (PPDM), and user privacy, pursued by private information retrieval (PIR). This special issue contains papers reporting recent advances in each of those three dimensions.

Part 7: Short Papers; International audience; Shamir proposed the setbase approach as a means of improving security and privacy of the traditional biometric system. In this paper, we propose privacy-preserving drawer size standards for the biometric setbase. The proposal incorporates database privacy metrics such as k-anonymity and l-diversity into the definition of privacy-preserving drawer size standard for the biometric setbase. We also empirically evaluate the system reliability of the prototype setbase for the purpose of studying the trade-off values between the level of privacy protection and the level of system security.

In contrast to classical cryptography, the challenge of privacy in the context of databases is to find a trade-off between a security guarantee and utility. Individuals in a database have to be protected while preseving the usefullnes of the data. In this paper, we provide an overview over the results in the field of database privacy with focus on privacy notions. On the basis of these notions, we provide a framework that allows for the definition meaningful guarantees based on the distribution on privacy breaches and sesitive predicates. Interestingly, these notions do not fulfill the privacy axioms defined by Kifer et al. in [1,2].

In this paper, we focus on the problem of private database release in the interactive setting: a trusted database curator receives queries in an online manner for which it needs to respond with accurate but privacy preserving answers. To this end, we generalize the IDC (Iterative Database Construction) framework of [15,13] that maintains a differentially private artificial dataset and answers incoming linear queries using the artificial dataset. In particular, we formulate a generic IDC framework based on the Mirror Descent algorithm, a popular convex optimization algorithm [1]. We then present two concrete applications, namely, cut queries over a bipartite graph and linear queries over low-rank matrices, and provide significantly tighter error bounds than the ones by [15,13].

Traditionally privacy preserving database sanitization utilizes some kind of systemic transformation of the original data set and maps the entries into a different set subject to specific utility and privacy requirements. It is obvious that the distribution of sensitive attributes in original database is domain specific and dependent on the application. Another factor that influences the choice of transformation method is the processing and publishing latency constraints, deterministic for handling the required complexity. In this paper, we propose an efficient way of database privacy protection algorithm which provides an optimal operating point for privacy preserving complexity while maintaining the required utility to minimize the information loss. Our algorithm is sensitive-attribute’s distribution aware and the optimization space is limited by the distribution type. Using the conventional k-anonymity and l-diversity techniques, we analyze and show the effectiveness of our proposed protocol, which outperforms the well-known methods in certain domain of database privacy preservation applications.

Real data used in research projects give rise to conflicts of protecting privacy and providing more functions: functionality requires trying to make use of maximum amount of data, while privacy requirements try to protect such information from being leaked. In this paper, a new model is proposed to capture relationships between database attributes and to record users’ knowledge about database so as to prevent them from getting sensitive information by combining results returned from multiple queries, aiming to protect database privacy. This new model successfully solves the deductive access control problem in databases.

Two different approaches to defining a notion of database privacy, the generalization method and the perturbation method, have been independently studied. These two approaches are significantly different, making it hard to compare related research. In this paper, we propose a unified model that is based on the perturbation method, but which is applicable to generalized data sets. In particular, this model applies the notion of differential privacy to data sets that satisfy $k$-anonymity. We demonstrate this approach through a simple case study. This is a first step towards a common notion for protecting database privacy.

Three integrity measures are introduced: contamination, channel suppression and program suppression. Contamination is a measure of how much untrusted information reaches trusted outputs; it is the dual of leakage, which is a measure of information-flow confidentiality. Channel suppression is a measure of how much information about inputs to a noisy channel is missing from the channel outputs. And program suppression is a measure of how much information about the correct output of a program is lost because of attacker influence and implementation errors. Program and channel suppression do not have interesting confidentiality duals. As a case study, a quantitative relationship between integrity, confidentiality and database privacy is examined.

This paper describes the author’s experience of infusing an introductory database course with privacy content, and the on-going project entitled Integrating Ethics Into the Database Curriculum, that evolved from that experience. The project, which has received funding from the National Science Foundation, involves the creation of a set of privacy modules that can be implemented systematically by database educators throughout the database design thread of an undergraduate course.

Record linkage is a technique for linking records of different files that correspond to the same individual. Traditional record linkage methods needs that the files have some common variables to permit such link. In this paper we study the possibility of applying record linkage techniques when the files do not share variables. In this case we establish links based on structural information. For extracting this structural information we use in this paper twofold integrals.