An improved Framework for Biometric Database’s privacy

Security and privacy are huge challenges in biometric systems. Biometrics are sensitive data that should be protected from any attacker and especially attackers targeting the confidentiality and integrity of biometric data. In this paper an extensive review of different physiological biometric techniques is provided. A comparative analysis of the various sus mentioned biometrics, including characteristics and properties is conducted. Qualitative and quantitative evaluation of the most relevant physiological biometrics is achieved. Furthermore, we propose a new framework for biometric database privacy. Our approach is based on the use of the promising fully homomorphic encryption technology. As a proof of concept, we establish an initial implementation of our security module using JAVA programming language.

Research Review on the Application of Homomorphic Encryption in Database Privacy Protection

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.

Cryptography: Data Encryption, Decryption and Compression, Database Privacy and Security, Zero Knowledge of Proof

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

Privacy and Synthetic Datasets

Sharing is a virtue, instilled in us from childhood. Unfortunately, when it comes to big data—i.e., databases possessing the potential to usher in a whole new world of scientific progress—the legal landscape prefers a hoggish motif. The historic approach to the resulting database–privacy problem has been anonymization, a subtractive technique incurring not only poor privacy results, but also lackluster utility. In anonymization’s stead, differential privacy arose; it provides better, near-perfect privacy, but is nonetheless subtractive in terms of utility. Today, another solution is leaning into the fore, synthetic data. Using the magic of machine learning, synthetic data offers a generative, additive approach—the creation of almost-but-not-quite replica data. In fact, as we recommend, synthetic data may be combined with differential privacy to achieve a best-of-both-worlds scenario. After unpacking the technical nuances of synthetic data, we analyze its legal implications, finding both over and under inclusive applications. Privacy statutes either overweigh or downplay the potential for synthetic data to leak secrets, inviting ambiguity. We conclude by finding that synthetic data is a valid, privacy-conscious alternative to raw data, but is not a cure-all for every situation. In the end, computer science progress must be met with proper policy in order to move the area of useful data dissemination forward.

Shopping for Privacy Online: Consumer Decision-making Strategies and the Emerging Market for Information Privacy

James P. Nehf, Shopping for Privacy Online: Consumer Decision-making Strategies and the Emerging Market for Information Privacy, 2005 Ill. J. L. Tech. & Pol'y 1Studies show that individuals are concerned about database privacy, yet they seldom make privacy a salient attribute when deciding among competing alternatives. Although privacy policies are present on many websites, web users rarely bother to read them. This paper explores why this is so. The author identifies rational reasons why web users do not shop for privacy and discusses the implications for the expanding market for consumer information. He concludes that unless privacy becomes a salient attribute influencing consumer choice, website operators will continue to obtain and use more personal information than web users would choose to provide in a more transparent exchange.

Quantification of integrity

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.