Disclosure Of Sensitive Information Research Articles

A Fair Mechanism for Private Data Publication in Online Social Networks

Due to the tremendous growth of online social networks in both participants and collected contents, social data publication has provided an opportunity for numerous services. However, neglectfully publishing all the contents leads to severe disclosure of sensitive information due to diverse user behaviors. Therefore, there should be a thoroughly designed framework for data publication in online social networks that considers users heterogeneous privacy preferences and the correlations among participants. This work proposes a novel mechanism for data publication that achieves high performance while preserving privacy and guaranteeing fairness among users. To derive the optimal scheme for data publication is NP-complete. Thus we propose a heuristic algorithm to determine the contents to be published which takes advantage of the sets of sensitive contents for each user and the correlation among them. The theoretical analysis proves the effectiveness and feasibility of the mechanism. The evaluations towards a real-world dataset reveal that the proposed algorithm outperforms the existing results.

How Primary Care Physicians Elicit Sensitive Health Information From Patients: Describing Access to Psychosocial Information

Multiple communication models describe factors that influence disclosure of sensitive health information. However, these models do not address the receiver's perspective of health-related information, nor do they address how the receiver promotes disclosure. In the primary care chronic disease visit, the patient (sender) must disclose sensitive health-related psychosocial information to the primary care physician (PCP) (receiver) for the PCP to understand potential barriers to care (e.g., financial strain) and make treatment decisions (e.g., referral to social work). A vital gap exists in understanding how PCPs perceive that patients disclose. We conducted individual, semi-structured interviews (n = 17) to understand PCPs' perceptions of patient disclosure of sensitive, psychosocial information in the clinical visit. PCPs facilitate patient disclosure by (a) building and maintaining rapport and (b) nurturing the patient-provider relationship. This article describes PCPs' perceptions of how they access psychosocial information which is vital to inform clinical decisions that facilitate personalized care.

A Sense of Privacy

Among the many factors that can elicit privacy concerns and affect privacy behavior, some are sensorial: detecting the presence of others through our senses. Human beings may be wired to react to sensorial cues and rely, in part, on them to assess the privacy ramifications of their actions. Individuals may react to sensorial cues indicating the presence of others even when those cues do not carry relevant information about likely consequences of privacy choices – and thus, from a normative perspective, may not be expected to influence privacy concerns and resulting behaviors. In four experiments (N = 829), we examine the effect on privacy-relevant behavior (the disclosure of sensitive personal information) of four sensorial cues signaling the presence of other humans: proximity, visual, auditory, and olfactory, each signaling the presence of another person. Proximity and visual cues (Experiments 1 and 2) produced an inhibitory effect on intimate self-disclosures in an online survey – including when that presence does not and cannot materially affect participants’ risks or benefits associated with disclosure (Experiment 2). Auditory and olfactory cues (Experiments 3 and 4), however, did not. The findings point to a possible influence of sensorial (specifically, visual) cues on privacy behavior. We discuss the implications of the findings in the context of privacy and security decision making in a digital age, where physical cues human beings may have adapted to use for detection of threats may be absent, or even strategically manipulated by antagonistic third parties.

Preserving differential privacy in deep neural networks with relevance-based adaptive noise imposition

In recent years, deep learning achieves remarkable results in the field of artificial intelligence. However, the training process of deep neural networks may cause the leakage of individual privacy. Given the model and some background information of the target individual, the adversary can maliciously infer the sensitive feature of the target individual. Therefore, it is imperative to preserve the sensitive information in the training data. Differential privacy is a state-of-the-art paradigm for providing the privacy guarantee of datasets, which protects the private and sensitive information from the attack of adversaries significantly. However, the existing privacy-preserving models based on differential privacy are less than satisfactory since traditional approaches always inject the same amount of noise into parameters to preserve the sensitive information, which may impact the trade-off between the model utility and the privacy guarantee of training data. In this paper, we present a general differentially private deep neural networks learning framework based on relevance analysis, which aims to bridge the gap between private and non-private models while providing an effective privacy guarantee of sensitive information. The proposed model perturbs gradients according to the relevance between neurons in different layers and the model output. Specifically, during the process of backward propagation, more noise is added to gradients of neurons that have less relevance to the model output, and vice-versa. Experiments on five real datasets demonstrate that our mechanism not only bridges the gap between private and non-private models, but also prevents the disclosure of sensitive information effectively.

The Relationship Between Interviewer-Respondent Rapport and Data Quality

Abstract Interviewer-respondent rapport is generally considered to be beneficial for the quality of the data collected in survey interviews; however, the relationship between rapport and data quality has rarely been directly investigated. We conducted a laboratory experiment in which eight professional interviewers interviewed 125 respondents to see how the rapport between interviewers and respondents is associated with the quality of data—primarily disclosure of sensitive information—collected in these interviews. It is possible that increased rapport between interviewers and respondents might motivate respondents to be more conscientious, increasing disclosure; alternatively, increased rapport might inhibit disclosure because presenting oneself unfavorably is more aversive if respondents have a positive relationship with the interviewer. More specifically, we examined three issues: (1) what the relationship is between rapport and the disclosure of information of varying levels of sensitivity, (2) how rapport is associated with item nonresponse, and (3) whether rapport can be similarly established in video-mediated and computer-assisted personal interviews (CAPIs). We found that (1) increased respondents’ sense of rapport increased disclosure for questions that are highly sensitive compared with questions about topics of moderate sensitivity; (2) increased respondents’ sense of rapport is not associated with a higher level of item nonresponse; and (3) there was no significant difference in respondents’ rapport ratings between video-mediated and CAPI, suggesting that rapport is just as well established in video-mediated interviews as it is in CAPI.

A privacy‐preserving density peak clustering algorithm in cloud computing

SummaryAiming at preventing the privacy disclosure of sensitive information, issues related to privacy protection in cloud computing have attracted the interest of researchers. To protect the privacy of users during clustering in a cloud computing environment, we present a privacy‐preserving density peak clustering (PPDPC) algorithm that neither discloses personal privacy information nor leaks the cluster centers. Our scheme contains two steps of density peak clustering: First, a cloud service provider calculates the cluster centers without knowing each participant's private data and without disclosing any cluster center information to the other participants, and second, participant allocation is secure and every participant is prevented from identifying the other members of the same cluster. Security analysis and comparison experiments show that the proposed PPDPC algorithm not only obtains good accuracy with respect to density peak clustering but also resists collusion attacks even if the cloud service provider is collaborating with all except one participant. Both theoretical analysis and experimental results confirm the security and accuracy of our method.

CFM: collusion-free model of privacy preserving frequent itemset mining

Although many privacy preserving frequent itemset mining protocols have been proposed to preserve the privacy of participants, most of them are vulnerable against collusion. Usually, these protocols are designed for semi-honest model; where in this model, it is assumed that the participants do not deviate from the protocol. However, in real world, participants may collude with each other in order to falsify the protocol or to obtain the secret values of other parties. In this paper, we analyse the vulnerability of previous privacy preserving frequent itemset mining protocols from privacy point of view, and then, we proposes a new protocol (CFM), which preserves the privacy of participants, even in collusion state. CFM is designed for mining frequent itemsets from homogenous (horizontally partitioned) data, which not only preserves the privacy of participants in collusion states, but also shows better performance in comparison with previous works. In order to achieve this goal, CFM employs a new secret sharing and secret summation scheme, which distributes secret values among participants. Privacy preserving level of CFM is evaluated based on the disclosure of sensitive information.

CFM: collusion-free model of privacy preserving frequent itemset mining

Although many privacy preserving frequent itemset mining protocols have been proposed to preserve the privacy of participants, most of them are vulnerable against collusion. Usually, these protocols are designed for semi-honest model; where in this model, it is assumed that the participants do not deviate from the protocol. However, in real world, participants may collude with each other in order to falsify the protocol or to obtain the secret values of other parties. In this paper, we analyse the vulnerability of previous privacy preserving frequent itemset mining protocols from privacy point of view, and then, we proposes a new protocol (CFM), which preserves the privacy of participants, even in collusion state. CFM is designed for mining frequent itemsets from homogenous (horizontally partitioned) data, which not only preserves the privacy of participants in collusion states, but also shows better performance in comparison with previous works. In order to achieve this goal, CFM employs a new secret sharing and secret summation scheme, which distributes secret values among participants. Privacy preserving level of CFM is evaluated based on the disclosure of sensitive information.

Data Security and Privacy Protection for Cloud Storage: A Survey

The new development trends including Internet of Things (IoT), smart city, enterprises digital transformation and world's digital economy are at the top of the tide. The continuous growth of data storage pressure drives the rapid development of the entire storage market on account of massive data generated. By providing data storage and management, cloud storage system becomes an indispensable part of the new era. Currently, the governments, enterprises and individual users are actively migrating their data to the cloud. Such a huge amount of data can create magnanimous wealth. However, this increases the possible risk, for instance, unauthorized access, data leakage, sensitive information disclosure and privacy disclosure. Although there are some studies on data security and privacy protection, there is still a lack of systematic surveys on the subject in cloud storage system. In this paper, we make a comprehensive review of the literatures on data security and privacy issues, data encryption technology, and applicable countermeasures in cloud storage system. Specifically, we first make an overview of cloud storage, including definition, classification, architecture and applications. Secondly, we give a detailed analysis on challenges and requirements of data security and privacy protection in cloud storage system. Thirdly, data encryption technologies and protection methods are summarized. Finally, we discuss several open research topics of data security for cloud storage.

Managing Privacy: Cryptography or Private Networks of Communication in the Nineteenth Century.

The issue of privacy in communication networks is not new; nor are the technical solutions that address the matter. This article examines how a broad range of organizations developed private telegraphy in response to privacy concerns in the nineteenth century. Over a century ago, the advent of electric telegraphy prompted the desire to send private messages over the wires. At the time, telegrams were like open letters, and such messages were visible to the clerks and operators who processed them. In response, entrepreneurs developed codebooks and cipher tools to prevent disclosure of sensitive information. Another popular method, as this article demonstrates, was the deployment of private telegraphic wires to enable peer-to-peer communication and prevent third party access. Private telegraphy, a new form of communication, was common in the United States and even more so in the United Kingdom in the late nineteenth century.

Privacy protection in mobile crowd sensing: a survey

The unprecedented proliferation of mobile smart devices has propelled a promising computing paradigm, Mobile Crowd Sensing (MCS), where people share surrounding insight or personal data with others. As a fast, easy, and cost-effective way to address large-scale societal problems, MCS is widely applied into many fields, e.g., environment monitoring, map construction, public safety, etc. Despite the popularity, the risk of sensitive information disclosure in MCS poses a serious threat to the participants and limits its further development in privacy-sensitive fields. Thus, the research on privacy protection in MCS becomes important and urgent. This paper targets the privacy issues of MCS and conducts a comprehensive literature research on it by providing a thorough survey. We first introduce a typical system structure of MCS, summarize its characteristics, propose essential requirements on privacy on the basis of a threat model. Then, we survey existing solutions on privacy protection and evaluate their performances by employing the proposed requirements. In essence, we classify the privacy protection schemes into four categories with regard to identity privacy, data privacy, attribute privacy, and task privacy. Besides, we review the achievements on privacy-preserving incentives in MCS from four viewpoints of incentive measures: credit incentive, auction incentive, currency incentive, and reputation incentive. Finally, we point out some open issues and propose future research directions based on the findings from our survey.

Using computer automated systems to conduct personal interviews: Does the mere presence of a human face inhibit disclosure?

Personal interviews are widely used to collect open-ended data in an array of settings, many of which require solicitation of data that may be regarded as sensitive or threatening by interviewees. The high costs of employing human interviewers can be mitigated by utilizing computer-generated “human-like” interviewers, at least in scripted interview conditions. However, the effects of replacing humans with human-like interviewers on interviewees’ level of actual disclosure has not been well-researched. This experiment compared disclosure of sensitive information obtained from personal interviews utilizing various interviewer modes. One-hundred and fifty-eight students from a southwestern university were randomly assigned to answer a series of open-ended questions in one of three different personal interview modes: 1) audio-only computer-assisted self-interview (i.e., ACASI); 2) human-like embodied conversational agent (i.e., ECA); or 3) a human interviewer. Disclosure was measured using both self-report and objective scores derived from trained judges. Disclosure levels were significantly higher in the faceless ACASI condition than in the combined virtual and human interviewer conditions – both of which were embodied with a face. There was no significant difference between the ECA and human interviewer conditions. This suggests that the mere presence of a human face can inhibit disclosure.

"I Don't Want Them to Not Know"

Dating platform research often focuses on people's decisions about when to reveal certain aspects of themselves to others, or self-disclosure. One example is deciding what to include in one's profile and what to reveal in chat conversations or in person. Transgender people face a particularly acute challenge in self-disclosure, but we know little about how they experience it on dating platforms. Revealing trans status can result in physical or emotional harm, but is also often considered necessary for a successful relationship and for self-fulfillment. To better understand disclosure of sensitive information, we interviewed 20 transgender dating platform users in the U.S. We find that direct, proactive disclosure of trans status was motivated by desires for safety and certainty, though this could involve tension. Physical separation and one-to-many communication surface as key affordances that facilitated disclosure. These results help us better understand motivations behind disclosure decisions.

Toward Addressing Location Privacy Issues: New Affiliations with Social and Location Attributes

Nowadays, location-sharing applications (LSA) within social media enable users to share their location information at different levels of precision. Users on their side are willing to disclose this kind of information in order to represent themselves in a socially acceptable online way. However, they express privacy concerns regarding potential malware location-sharing applications, since users’ geolocation information can provide affiliations with their social identity attributes that enable the specification of their behavioral normativity, leading to sensitive information disclosure and privacy leaks. This paper, after a systematic review on previous social and privacy location research, explores the overlapping of these fields in identifying users’ social attributes through examining location attributes while online, and proposes a targeted set of location privacy attributes related to users’ socio-spatial characteristics within social media.

Random dictatorship for privacy-preserving social choice

Social choice provides methods for collective decisions. They include methods for voting and for aggregating rankings. These methods are used in multiagent systems for similar purposes when decisions are to be made by agents. Votes and rankings are sensitive information. Because of that, privacy mechanisms are needed to avoid the disclosure of sensitive information. Cryptographic techniques can be applied in centralized environments to avoid the disclosure of sensitive information. A trusted third party can then compute the outcome. In distributed environments, we can use a secure multiparty computation approach for implementing a collective decision method. Other privacy models exist. Differential privacy and k-anonymity are two of them. They provide privacy guarantees that are complementary to multiparty computation approaches, and solutions that can be combined with the cryptographic ones, thus providing additional privacy guarantees, e.g., a differentially private multiparty computation model. In this paper, we propose the use of probabilistic social choice methods to achieve differential privacy. We use the method called random dictatorship and prove that under some circumstances differential privacy is satisfied and propose a variation that is always compliant with this privacy model. Our approach can be implemented using a centralized approach and also a decentralized approach. We briefly discuss these implementations.

Use of Short Messaging Service to Improve Follow-Up for Abnormal Pap Test Results in Minority and Medically Underserved Women in North Carolina: Questionnaire on Attitudes and Acceptability.

BackgroundAn estimated one in eight cervical cancer cases are due to a lack of follow-up care for abnormal Pap test results. Low rates of completion of follow-up care particularly affect low-income minority women. The burden of cervical cancer could be reduced through interventions that improve timely colposcopy follow-up and treatment of abnormal screening results. Mobile communications via text messaging present a low-cost opportunity to increase rates of clinic return among women referred to follow-up after obtaining abnormal screening results.ObjectiveOur aims were to determine the acceptability and feasibility of using text messaging to increase completion of follow-up care following abnormal cervical cancer screening (Pap test) results and to examine factors that may affect the acceptability and use of text messaging to increase communications between health care providers (HCP) and low-income minority women.MethodsThe study participants were 15 low-income women who had undergone a Pap test within the preceding 12 months. Semistructured interviews, including open- and closed-ended questions from a validated questionnaire, were conducted by phone or in person. Responses to closed-ended survey items were tabulated, and descriptive statistics were generated using Microsoft Excel. Responses to the open-ended questions were coded and analyzed using NVivo 11 qualitative analysis software.ResultsNearly all participants (14/15, 93%) were comfortable receiving a text message from an HCP stating that their Pap test results were available (<40 years: 100%; ≥40 years: 86%). Over half (8/15; 53%) of the participants were comfortable receiving a text message stating that their Pap test results were abnormal, although many preferred to receive such information via a phone call (6/15; 40%). Most participants (9/15; 60%) believed that receiving a text reminder would make them more likely to attend their appointment. The preferred method for receiving a reminder appeared to vary by age, with older women preferring telephone reminders over text messaging reminders. Analysis of open-ended questions suggested that text messaging appeals to some women due to its wide use and convenience for communicating with HCPs. However, women cited concerns about the confidentiality of messages and barriers to understanding the messages, including the physical capacity to read and accurately interpret the content of the messaging.ConclusionsMost participants indicated a willingness to receive text messages from their HCPs about cervical cancer screening results and believed that text messages were the best way to remind them of appointments for follow-up care. Potential concerns could be addressed by excluding explicit references to the nature of the appointment in the text message in order to avoid disclosure of sensitive health information to unauthorized individuals. Although text messaging seems promising to improve adherence to timely follow-up, personal preferences should be considered by allowing patients to opt-out of text communications.

Evidence in the Age of Privacy: Access to Data in the Criminal Justice System

This Article introduces the phenomenon of “privacy asymmetries,” which are privacy statutes that permit courts to order disclosures of sensitive information when requested by law enforcement, but not when requested by criminal defense counsel. In the United States adversarial criminal legal system, defense counsel are the sole actors tasked with investigating evidence of innocence. Law enforcement has no constitutional, statutory, or formal ethical duty to seek out evidence of innocence. Therefore, selectively suppressing defense investigations means selectively suppressing evidence of innocence. Privacy asymmetries form a recurring, albeit previously unrecognized, pattern in privacy statutes. They likely arise from legislative oversight and not reasoned deliberation. Worse, they risk unnecessary harms to criminal defendants, as well as to the truth-seeking process of the judiciary, by advantaging the search for evidence of guilt over that for evidence of innocence. The number of these harms will only increase in the digital economy as private companies collect immense quantities of data about our heart beats, movements, communications, consumption, and more. Much of that data will be relevant to criminal investigations, and available to the accused solely through the very defense subpoenas that privacy asymmetries block. Moreover, the introduction of artificial intelligence and machine learning tools into the criminal justice system will exacerbate the consequences of law enforcement’s and defense counsel’s disparate access to data. To avoid enacting privacy asymmetries by sheer accident, legislators drafting privacy statutes should include a default symmetrical savings provision for law enforcement and defense investigators alike.

Efficient privacy‐preserving scheme supporting disjunctive multi‐keyword search with ranking

SummaryInformation storage and retrieval from the cloud is growing continuously unabated. The cost‐efficient solutions offered by the cloud providers to the end‐users have motivated them to outsource their confidential data to the cloud. Outsourcing confidential data leads to enhanced privacy risks due to disclosure of sensitive information to adversaries. To handle this disclosure of information, encryption is preferred, but it hinders the efficient searching on the documents. The existing searchable encryption schemes either focused on optimization of search time or improvement of search efficiency. To solve this trade‐off between search time and search efficiency, we propose an efficient disjunctive search scheme using non‐positional inverted index. To the best of our knowledge, there is no searchable encryption scheme based on the non‐positional inverted index in the literature. Thus, we first propose a basic scheme based on a non‐positional inverted index to search the desired keywords and highlight its inefficiency in terms of high search time required. To perform efficient searching, an extended search scheme is proposed using keyword binning, which reduces comparisons required and improves the search time. The extended search scheme has recall of 100% and precision 99.75%. The experimental analysis of the proposed scheme on real datasets proves that the proposed scheme is privacy‐preserving and efficient.

Barrier analysis of supply chain finance adoption in manufacturing companies

PurposeCompanies all over the world have recently started to adopt supply chain finance (SCF) solutions in their supply chains to reduce the payment defaults and simplify the bill settlement process. The purpose of this paper is to identify and prioritize the barriers to adopting SCF in micro, small and medium enterprises.Design/methodology/approachIt employs a three-phase methodology to identify and prioritize the essential barriers to the implementation of SCF. An extensive survey has been carried out in 101 Indian MSMEs in India which identified 37 barriers under six heads in the first phase. Experts’ interview using the Delphi technique has been carried out in the second phase to finalize the barriers. The analytic hierarchy process methodology, with sensitivity analysis for validation, is used in the final stage to prioritize and rank the barriers.FindingsResults show that financial and information technology barriers are prominent in SCF adoption followed by financial challenges. Among specific barriers, the disclosure of sensitive company information to competitor barrier acts as an essential barrier followed by poor technological capability of MSMEs.Research limitations/implicationsThe study is limited to SCF adoption of MSMEs in a developing nation. Extensive research is required in order to derive a global trend.Practical implicationsThe current research contributes to the stakeholder theory and transaction cost economics. Observations made in the current research can encourage organizations to incorporate stakeholders’ concerns into the adoption of SCF solutions. The study provides a more in-depth view of such challenges and a benchmark, which will help companies to adopt SCF solutions more effortlessly. Moreover, policy makers across the world can explore these serious issues and amend or introduce new policies to facilitate companies’ implementation of supply chain financial solutions.Originality/valueTo the best of the authors’ knowledge, this is the first study which identified and prioritized SCF adoption barriers of MSMEs in a developing nation. This study is also novel in adopting a hybrid analytical hierarchy process-sensitivity analysis for ranking the SCF barriers in an MSME context. SCF studies often emphasize only on the reverse factoring aspect of SCF. The current study considers many innovative aspects of SCF, such as pre-shipment financing, dynamic discounting, inventory financing, collaborative logistics, etc.

Conditional Masking to Numerical Data

Protecting the privacy of datasets has become hugely important these days. Many real-life datasets like income data and medical data need to be secured before making it public. However, security comes at the cost of losing some useful statistical information about the dataset. Data obfuscation deals with this problem of masking a dataset in such a way that the utility of the data is maximized while minimizing the risk of the disclosure of sensitive information. Two popular approaches to data obfuscation for numerical data involve (i) data swapping and (ii) adding noise to data. While the former masks well sacrificing the whole of correlation information, the latter gives estimates for most of the popular statistics like mean, variance, quantiles and correlation but fails to give an unbiased estimate of the distribution curve of the original data. In this paper, we propose a mixed method of obfuscation combining the above two approaches and discuss how the proposed method succeeds in giving an unbiased estimation of the distribution curve while giving reliable estimates of the other well-known statistics like moments and correlation.