Rebecca N. Wright

Bio: Rebecca N. Wright is an academic researcher from Rutgers University. The author has contributed to research in topics: Secure multi-party computation & Cryptography. The author has an hindex of 37, co-authored 113 publications receiving 4722 citations. Previous affiliations of Rebecca N. Wright include AT&T Labs & Alcatel-Lucent.

Privacy-preserving distributed k-means clustering over arbitrarily partitioned data

Abstract: Advances in computer networking and database technologies have enabled the collection and storage of vast quantities of data. Data mining can extract valuable knowledge from this data, and organizations have realized that they can often obtain better results by pooling their data together. However, the collected data may contain sensitive or private information about the organizations or their customers, and privacy concerns are exacerbated if data is shared between multiple organizations.Distributed data mining is concerned with the computation of models from data that is distributed among multiple participants. Privacy-preserving distributed data mining seeks to allow for the cooperative computation of such models without the cooperating parties revealing any of their individual data items. Our paper makes two contributions in privacy-preserving data mining. First, we introduce the concept of arbitrarily partitioned data, which is a generalization of both horizontally and vertically partitioned data. Second, we provide an efficient privacy-preserving protocol for k-means clustering in the setting of arbitrarily partitioned data.

Privacy-preserving Bayesian network structure computation on distributed heterogeneous data

Abstract: As more and more activities are carried out using computers and computer networks, the amount of potentially sensitive data stored by business, governments, and other parties increases. Different parties may wish to benefit from cooperative use of their data, but privacy regulations and other privacy concerns may prevent the parties from sharing their data. Privacy-preserving data mining provides a solution by creating distributed data mining algorithms in which the underlying data is not revealed.In this paper, we present a privacy-preserving protocol for a particular data mining task: learning the Bayesian network structure for distributed heterogeneous data. In this setting, two parties owning confidential databases wish to learn the structure of Bayesian network on the combination of their databases without revealing anything about their data to each other. We give an efficient and privacy-preserving version of the K2 algorithm to construct the structure of a Bayesian network for the parties' joint data.

Privacy-Preserving Classification of Customer Data without Loss of Accuracy.

Abstract: Privacy has become an increasingly important issue in data mining. In this paper, we consider a scenario in which a data miner surveys a large number of customers to learn classiflcation rules on their data, while the sensitive attributes of these customers need to be protected. Solutions have been proposed to address this problem using randomization techniques. Such solutions exhibit a tradeofi of accuracy and privacy: the more each customer’s private information is protected, the less accurate result the miner obtains; conversely, the more accurate the result, the less privacy for the customers. In this paper, we propose a simple cryptographic approach that is e‐cient even in a many-customer setting, provides strong privacy for each customer, and does not lose any accuracy as the cost of privacy. Our key technical contribution is a privacy-preserving method that allows a data miner to compute frequencies of values or tuples of values in the customers’ data, without revealing the privacy-sensitive part of the data. Unlike general-purpose cryptographic protocols, this method requires no interaction between customers, and each customer only needs to send a single ∞ow of communication to the data miner. However, we are still able to ensure that nothing about the sensitive data beyond the desired frequencies is revealed to the data miner. To illustrate the power of our approach, we use our frequency mining computation to obtain a privacypreserving naive Bayes classifler learning algorithm. Initial experimental results demonstrate the practical e‐ciency of our solution. We also suggest some other applications of privacy-preserving frequency mining.

Privacy-enhancing k-anonymization of customer data

Abstract: In order to protect individuals' privacy, the technique of k-anonymization has been proposed to de-associate sensitive attributes from the corresponding identifiers. In this paper, we provide privacy-enhancing methods for creating k-anonymous tables in a distributed scenario. Specifically, we consider a setting in which there is a set of customers, each of whom has a row of a table, and a miner, who wants to mine the entire table. Our objective is to design protocols that allow the miner to obtain a k-anonymous table representing the customer data, in such a way that does not reveal any extra information that can be used to link sensitive attributes to corresponding identifiers, and without requiring a central authority who has access to all the original data. We give two different formulations of this problem, with provably private solutions. Our solutions enhance the privacy of k-anonymization in the distributed scenario by maintaining end-to-end privacy from the original customer data to the final k-anonymous results.

Off-Line Generation of Limited-Use Credit Card Numbers

Abstract: Recently, some credit card companies have introduced limited-use credit card numbers--for example, American Express's single-use card numbers and Visa's gift cards. Such limited-use credit cards limit the exposure of a traditional long-term credit card number, particularly in Internet transactions. These offerings employ an on-line solution, in that a credit card holder must interact with the credit card issuer in order to derive a limited-use token. In this paper, we describe a method for cryptographic off-line generation of limited-use credit card numbers. This has several advantages over the on-line schemes, and it has applications to calling cards as well. We show that there are several trade-offs between security and maintaining the current infrastructure.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

The Algorithmic Foundations of Differential Privacy

Abstract: The problem of privacy-preserving data analysis has a long history spanning multiple disciplines. As electronic data about individuals becomes increasingly detailed, and as technology enables ever more powerful collection and curation of these data, the need increases for a robust, meaningful, and mathematically rigorous definition of privacy, together with a computationally rich class of algorithms that satisfy this definition. Differential Privacy is such a definition.After motivating and discussing the meaning of differential privacy, the preponderance of this monograph is devoted to fundamental techniques for achieving differential privacy, and application of these techniques in creative combinations, using the query-release problem as an ongoing example. A key point is that, by rethinking the computational goal, one can often obtain far better results than would be achieved by methodically replacing each step of a non-private computation with a differentially private implementation. Despite some astonishingly powerful computational results, there are still fundamental limitations — not just on what can be achieved with differential privacy but on what can be achieved with any method that protects against a complete breakdown in privacy. Virtually all the algorithms discussed herein maintain differential privacy against adversaries of arbitrary computational power. Certain algorithms are computationally intensive, others are efficient. Computational complexity for the adversary and the algorithm are both discussed.We then turn from fundamentals to applications other than queryrelease, discussing differentially private methods for mechanism design and machine learning. The vast majority of the literature on differentially private algorithms considers a single, static, database that is subject to many analyses. Differential privacy in other models, including distributed databases and computations on data streams is discussed.Finally, we note that this work is meant as a thorough introduction to the problems and techniques of differential privacy, but is not intended to be an exhaustive survey — there is by now a vast amount of work in differential privacy, and we can cover only a small portion of it.