The practice of sharing online user information with external third parties has become the focal point of privacy concerns for consumer advocacy groups and policy makers. We explore the decisions by websites regarding the obfuscation that they use to make it difficult for users to discover the extent of information sharing. Using a Bayesian model, we shed light on the websites’ incentive to obfuscate user information sharing. We find that as content sensitivity increases, a website reduces its level of obfuscation. Furthermore, more popular websites engage in higher levels of obfuscation than less popular ones. We provide an empirical analysis of obfuscation and user information sharing in News (low content sensitivity) and Health (high content sensitivity) websites and confirm key results from our analytical model. Our analysis illustrates that obfuscation of information sharing is a viable strategy that websites use to improve their profits. History: Ram Ramesh, area editor for Data Science & Machine Learning. Funding: Financial support from the Social Sciences and Humanities Research Council of Canada is gratefully acknowledged. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.1266 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2021.0070 ) at http://dx.doi.org/10.5281/zenodo.7336098 .

Product assortment and availability are important determinants of sales success for firms of industrial commodity products. Well-known pricing and promotion strategies for differentiated products do not translate well to such products where price is closely tied to the cost of the products. Consequently, firms with multiple stores of commodity products are faced with the problem of product assortment that incorporates varying geographic and demographic conditions of locations they serve. The paper presents a model for assortment planning and optimization for multiple stores of a company. The novelty of our approach is twofold: first, it deploys data mining techniques to identify sales pattern information across multiple stores through existing sales data across segments and across stores; second, it identifies the optimal product assortment for each store and permits analyses of assortment efficiency evaluation among all existing stores. Our model first finds frequent itemsets based on association rule analysis and prunes them using a novel conflict resolution method. It then incorporates the identified product combinations into the development of the optimization formulation. Our methodology offers solutions that have important implications on product assortment, including complements versus substitutes and product bundling, and sheds lights on product planning and assortment strategies in general. A data set from an industry leading plastics manufacturer and retailer in the United States is used to demonstrate our model.

OBJECTIVE: The objective of this research is to empirically explore the drivers of patients' consent to sharing of their medical records on health information exchange (HIE) platforms. MATERIALS AND METHODS: The authors analyze a dataset consisting of consent choices of 20,076 patients in Western New York. A logistic regression is applied to empirically investigate the effects of patients' age, gender, complexity of medical conditions, and the role of primary care physicians on patients' willingness to disclose medical information on HIE platforms. RESULTS: The likelihood of providing consent increases by age (odds ratio (OR) = 1.055; P < .0001). Female patients are more likely to provide consent (OR = 1.460; P = .0003). As the number of different physicians involved in the care of the patient increases, the odds of providing consent slightly increases (OR = 1.024; P = .0031). The odds of providing consent is significantly higher for the patients whom a primary care physician has been involved in their medical care (OR = 1.323; P < .0001). CONCLUSION: Individual-level characteristics are important predictors of patients' willingness to disclose their medical information on HIE platforms.

BACKGROUND AND OBJECTIVE: We postulate that professional proximity due to common patients and geographical proximity among practice locations are significant factors influencing the adoption of health information exchange (HIE) services by healthcare providers. The objective of this study is to investigate the direct and indirect network effects of these drivers on HIE diffusion. DESIGN: Multi-dimensional scaling and clustering are first used to create different clusters of physicians based on their professional and geographical proximities. Extending the Bass diffusion model to capture direct and indirect network effects among groups, the growth of HIE among these clusters is modeled and studied. The network effects among the clusters are investigated using adoption data over a 3-year period for an HIE based in Western New York. MEASUREMENT: HIE adoption parameters-external sources of influence as well as direct and indirect network coefficients-are estimated by the extended version of the Bass diffusion model. RESULTS: Direct network effects caused by common patients among physicians are much more influential on HIE adoption as compared with previously investigated social contagion and external factors. Professional proximity due to common patients does influence adoption decisions; geographical proximity is also influential, but its effect is more on rural than urban physicians. CONCLUSIONS: Flow of patients among different groups of physicians is a powerful factor in HIE adoption. Rather than merely following the market trend, physicians appear to be influenced by other physicians with whom they interact with and have common patients.

Our work addresses internal information breaches that emanate from organizational workflows. Information breaches are particularly piquant in organizational workflows, as the underlying tasks constitute natural points where private information on individuals is accessed to execute the workflows. Our work builds on and extends the widely used role-based access controls by considering processwide security considerations to both optimize the efficiency of workflow staffing and minimize data exposure in complex workflows. We employ a Jackson queueing network modeling framework, which allows both predictable and stochastic variability as well as varied employee skill sets. This framework enables the modeling of internal security threats that emanate from cross-task and cross-personnel assignments and the development of optimal staffing strategies that meet security requirements at minimum operational costs. Our detailed implementation analysis reveals that the model developed is not demanding in terms of required parameters and that the proposed approach is practical and adaptable to evolving business, regulatory, and workforce conditions. Our model is applicable to any digital transformation that involves confidential data sequences that carry security vulnerability, as is often the case in many settings such as health care, online banking, electronic payment systems, and interorganizational data interchange.

The massive amount of sensitive survey data about individuals that agencies collect and share through the Internet is causing a great deal of privacy concerns. These concerns may discourage individuals from revealing their sensitive information. Existing data collection techniques have serious downsides in terms of both efficiency and the levels of protection they offer against various realizations of threats. Moreover, they do not provide any flexibility to the users to be able to specify acceptable levels of privacy protection before deciding whether to participate in the surveys. In this paper, we propose a two-pronged privacy protection model corresponding to these two privacy concerns: these are a new efficient anonymity preserving data collection technique and a method to incorporate heterogeneous privacy constraints. Together, they help preserve the privacy of respondents both during and after data collection.

Confidentiality via Camoufiage (CVC) provides a practical method for giving unlimited, correct, numerical responses to ad-hoc queries to an on-line database, while not compromising confidential numerical data. CVC-POL guarantees confidentiality by hiding protected data points in a polytope and generating query answers over the polytope. In this paper we introduce CVC-STAR, which protects confidential data using a set defined by a union of line segments resembling a star. Solutions for determining CVC-STAR interval answers are given for several common query types. Computational experience shows that the answers for CVC-STAR are superior to those of CVC-POL while providing more robust confidentiality protection.

On-demand streaming from a remote server through best-effort Internet poses several challenges because of network losses and variable delays. The primary technique used to improve the quality of distributed content service is replication. In the context of the Internet, Web caching is the traditional mechanism that is used. In this article we develop a new staged delivery model for a distributed architecture in which video is streamed from remote servers to edge caches where the video is buffered and then streamed to the client through a last-mile connection. The model uses a novel revolving indexed cache buffer management mechanism at the edge cache and employs selective retransmissions of lost packets between the remote and edge cache for a best-effort recovery of the losses. The new Web cache buffer management scheme includes a dynamic adjustment of cache buffer parameters based on network conditions. In addition, performance of buffer management and retransmission policies at the edge cache is modeled and assessed using a probabilistic analysis of the streaming process as well as system simulations. The influence of different endogenous control parameters on the quality of stream received by the client is studied. Calibration curves on the QoS metrics for different network conditions have been obtained using simulations. Edge cache management can be done using these calibration curves. ISPs can make use of calibration curves to set the values of the endogenous control parameters for specific QoS in real-time streaming operations based on network conditions. A methodology to benchmark transmission characteristics using real-time traffic data is developed to enable effective decision making on edge cache buffer allocation and management strategies.

Technological advances in the collection, storage, and analysis of data have increased the ease with which businesses can make profitable use of information about individuals. Some of this information is private, and individuals are simultaneously becoming more aware of the value of the information and how the loss of control over this information impacts their personal privacy. As a partial solution to these concerns, this paper presents a mechanism that serves two purposes. The first enables the use of private, numerical data in the answering of queries while simultaneously providing a security feature that protects the data owners from a loss of privacy that could result from an unauthorized access. The second develops a compensation model for the use of the data that allows individuals to dynamically redefine their security requirements. The compensation model is built on the information-security mechanism to create the foundation of a market for private information. This paper illustrates how compensation models like the one presented here could be used in a self-regulating market for private information. Additionally, the compensation component of an intermediated market for private information is developed and extensively analyzed. Finally, this paper provides insights and draws several important conclusions on markets for private information

Novel online file sharing technologies have created new market dynamics for the online distribution of digital goods. But the new potential benefits for consumers are juxtaposed against challenges and opportunities for sellers of such goods. Here we investigate one type of digital experience good, music, whose markets include the presence of piracy options. We present five different pricing models running from a base case of a traditional brick and mortar retailer not facing a piracy option to an online retailer offering per unit and subscription pricing but facing piracy alternatives. In addition, simulation results are presented as a vehicle to develop insights related to the model implications on revenue-maximization and piracy conditions.

The focus of this work is on join optimization in relational database systems. The importance of join optimization is critically underscored by the high cost of relational joins and their frequent needs in traditional as well as emerging database applications. We demonstrate that the sequence in which the pages of the relations are accessed to process a join is a critical determinant of the join execution cost and an optimization of this sequence can lead to a significant improvement in performance over the traditional approaches. We initially develop three network structures to represent a join on two relations: page connectivity graph, cascade and block tree cascade. A page connectivity graph is a bipartite representation of the set of connected pages in the two relations according to the join predicate. To reveal the structural properties of the join, the nodes of the bipartite graph are ordered into a set of levels, and the resulting isomorphic structure is termed a cascade. From the cascade, a tree structure termed a block tree cascade is derived by selectively grouping nodes at each level of the cascade into blocks. We formulate the join as a tree traversal process, and accordingly develop efficient tree traversal algorithms. We develop a compact data structure to store the resulting access path, and provide a comprehensive analysis of the algorithms with detailed assessments of their performance. The performance evaluation demonstrates that the proposed approach can result in significant cost savings over the current join processing methods, for low to modest values of the join selectivity factor.

Join processing in relational database systems continues to be a difficult and challenging problem. In this research, we propose a criss-cross hash join strategy that draws from both hashing and indexing techniques, inheriting the advantages of each. To facilitate the criss-cross hash join, a simple data structure, termed page map, is introduced. The page maps aid in reducing the hashing effort incurred in the current hash based join methods. Furthermore, the page maps implicitly capture and exploit the possible inherent order among tuples in the relations, however partial it may be, to achieve superior performance. As the proposed methodology relies on the hashing scheme, the page maps are simpler, more compact, and easier to maintain than the traditional data structures associated with index based join methods. We develop the ideas intuitively first, followed by a formal development of the concepts and the algorithms. A detailed probabilistic analysis of the algorithms is presented and their performance is assessed through extensive empirical investigations. The empirical analysis suggests significant performance improvements over the current state-of-the-art hybrid hash method, especially in the presence of possible inherent order.

We deal with the question of how to maintain security of confidential information in a database while answering as many queries as possible. The database is assumed to operate in a query restriction (as opposed to perturbation) mode in which exact answers are given to those queries which, together with those already answered, will not compromise any confidential datum. Those which fail this criterion are not answered. We introduce the concept of interval disclosure where a datum is compromised if the answered queries provide enough information to establish that it is contained in a given interval even if the datum cannot be determined exactly. Models are presented for the problem of deciding whether to answer a query and three techniques, one based on linear programming, are developed and tested.

The objective of access path optimization in relational joins is to minimize the number of pages accessed in performing a join, since the cost of data retrieval from disk accounts for most of the join processing costs. We develop an efficient approach to access path optimization by modeling an access path as a tree traversal process. Two join algorithms are developed using this model. The access path is stored as a compact index relation, which requires absolutely minimal storage. The join is easily materialized by reading the index relation sequentially, and performing the operations coded in its tuples in their serial order. The proposed approach is shown to result in globally minimal page accesses in certain classes of joins. Using a detailed probabilistic analysis and computational study, the proposed approach is shown to be much superior to the state-of-the-art join optimization methods for a general class of joins. INFORMS Journal on Computing, ISSN 1091-9856, was published as ORSA Journal on Computing from 1989 to 1995 under ISSN 0899-1499.

In this research, we address the query clustering problem which involves determining globally optimal execution strategies for a set of queries. The need to process a set of queries together often arises in deductive database systems, scientific database systems, large bibliographic retrieval systems and several other database applications. We address the optimization problem from the perspective of overlaps in data requirements, and model the batched operations using a set-partitioning approach. In this model, we first consider the case of m queries each involving a two-way join operation. We develop a recursive methodology to determine all the processing strategies in this case. Next, we establish certain dominance properties among the strategies, and develop exact as well as heuristic algorithms for selecting an appropriate strategy. We extend this analysis to a clustering approach, and outline a framework for optimizing multiway joins. The results show that the proposed approach is viable and efficient, and can easily be incorporated into the query processing component of most database systems.