Privacy Checks Research Articles

Health Systems Need to Transform Data Collection to Advance Health Equity.

Health Systems Need to Transform Data Collection to Advance Health Equity.

Subverting Privacy-Preserving GANs: Hiding Secrets in Sanitized Images

Unprecedented data collection and sharing have exacerbated privacy concerns and led to increasing interest in privacy-preserving tools that remove sensitive attributes from images while maintaining useful information for other tasks. Currently, state-of-the-art approaches use privacy-preserving generative adversarial networks (PP-GANs) for this purpose, for instance, to enable reliable facial expression recognition without leaking users' identity. However, PP-GANs do not offer formal proofs of privacy and instead rely on experimentally measuring information leakage using classification accuracy on the sensitive attributes of deep learning (DL)-based discriminators. In this work, we question the rigor of such checks by subverting existing privacy-preserving GANs for facial expression recognition. We show that it is possible to hide the sensitive identification data in the sanitized output images of such PP-GANs for later extraction, which can even allow for reconstruction of the entire input images, while satisfying privacy checks. We demonstrate our approach via a PP-GAN-based architecture and provide qualitative and quantitative evaluations using two public datasets. Our experimental results raise fundamental questions about the need for more rigorous privacy checks of PP-GANs, and we provide insights into the social impact of these.

Knapsack graph-based privacy checking for smart environments

A smart environment consisting of sensors and actuators that network a physical space makes the lives of users extremely convenient. In such environments, service providers collect, store, and process a huge amount of personal information from the users, and provide appropriate services in return. Processing personal information helps service providers dispense personalized services to users. The flow of personal information requires legal documents such as privacy preferences and privacy policies from the users and service providers, respectively. A software agent (to be installed either at the client’s or server’s end) performs the privacy checking (i.e., matches the service provider’s privacy policies with the user’s privacy preferences), and produces a decision based on a “take it or leave it” approach. Often, this check is too restrictive such that the user is denied the services, even if the privacy policies partially satisfy the user preferences. In this work, the privacy checking problem is envisaged as a knapsack problem, and to address it, we derive (i) knapsack privacy checking (KPC), and (ii) knapsack graph-based privacy checking (KPC-G) techniques. The system developed using these techniques finds the services that best match the user’s privacy preferences as well as the service provider’s policies, with a twofold functionality: (a) it recommends the best matching services based on user-supplied tolerance values, or (b) it solves an optimization problem to suggest services after adjusting the user-supplied tolerance values. Our experimental results demonstrate the effectiveness of the proposed model.

E-Commerce Interfering With Privacy: Perceived Risks and Security Issues With Techno-Policy Outcomes

Privacy with the alignment to the individual’s prerogative harvests with the legal protection and socio-legal attributes that are majorly connected with the freedom and choice of life that needs to be unmingled without slightest external interferences or unwarranted observance. In the present paper, privacy risks are associated with the e-commerce platforms and by the time we attain an unfettered check over privacy violations or third-party control on these e-commerce entities, there should be a decisive movement prolonged towards securing the possible means to curb the privacy violations. It is very likely that the privacy could be subjective or context drove which could be put in the words as “contextual integrity” and what information is shared (appropriateness) with reference to a context and how far the revelation of such consumer data is streamed (flow or distribution) in a context could be a defining line for privacy, especially when privacy seems to be intertwined with the transparency. The paper examines the challenges that the privacy tends to present before economies, in a very generalized sense of its effect on social turf and particularly on side of risk attributes that e-commerce serves towards privacy creating privacy perils. The banking industry and its effect on the e-commerce transition with relative privacy checks have also been briefly touched upon, seeming the strong and indissoluble knot between them. The paper also analyses the constructed term “Behavioural Mapping” that presents an approach over effect on customers when behavioural attributes collected by e-commerce are misused while also reaching towards the effect of customer response towards the e-business practices. The paper has given due approach towards the regulatory and security-related issues while examining the approach towards perceived privacy among customers along with several other factors such as trust, integrity etc. It is duly noted that the security being itself an independent factor affected by e-privacy and effector as well, however, is relatively analysed with privacy in many places due to their proximate relation in respect to e-commerce- an exponent duly explained and satisfied in the present paper. Privacy enhancing technologies, privacy seals, trust certificate and assurances have been suggested as the near possible solution to minimize privacy risk at e-commerce platforms, while also there has been a suggestive realm left open to encounter giant e-commerce houses to build safety mechanism apart from governmental regulation and support, while also making possible room for developing and accessing technological infrastructure for others in business for measuring and reducing the perceived privacy risk in e-business platforms.

Secure open cloud in data transmission using reference pattern and identity with enhanced remote privacy checking

To handle the large amount of client’s data in open cloud lots of security issues need to be address. Client’s privacy should not be known to other group members without data owner’s valid permission. Sometime clients are fended to have accessing with open cloud servers due to some restrictions. To overcome the security issues and these restrictions related to storing, data sharing in an inter domain network and privacy checking, we propose a model in this paper which is based on an identity based cryptography in data transmission and intermediate entity which have client’s reference with identity that will take control handling of data transmission in an open cloud environment and an extended remote privacy checking technique which will work at admin side. On behalf of data owner’s authority this proposed model will give best options to have secure cryptography in data transmission and remote privacy checking either as private or public or instructed. The hardness of Computational Diffie–Hellman assumption algorithm for key exchange makes this proposed model more secure than existing models which are being used for public cloud environment.

FakeMask: A Novel Privacy Preserving Approach for Smartphones

Users can enjoy personalized services provided by various context-aware applications that collect users’ contexts through sensor-equipped smartphones. Meanwhile, serious privacy concerns arise due to the lack of privacy preservation mechanisms. Currently, most mechanisms apply passive defense policies in which the released contexts from a privacy preservation system are always real, leading to a great probability with which an adversary infers the hidden sensitive contexts about the users. In this paper, we apply a deception policy for privacy preservation and present a novel technique, FakeMask, in which fake contexts may be released to provably preserve users’ privacy. The output sequence of contexts by FakeMask can be accessed by the untrusted context-aware applications or be used to answer queries from those applications. Since the output contexts may be different from the original contexts, an adversary has greater difficulty in inferring the real contexts. Therefore, FakeMask limits what adversaries can learn from the output sequence of contexts about the user being in sensitive contexts, even if the adversaries are powerful enough to have the knowledge about the system and the temporal correlations among the contexts. The essence of FakeMask is a privacy checking algorithm which decides whether to release a fake context for the current context of the user. We present a novel privacy checking algorithm and an efficient one to accelerate the privacy checking process. Extensive evaluation experiments on real smartphone context traces of users demonstrate the improved performance of FakeMask over other approaches.

Patient privacy and sleepless nights

While walking through the hospital the other day, I overheard two employees talking about a "problem patient." Even though no patient name or location was mentioned in the conversation, the employees did discuss some of the patient's history. After gently but firmly reminding the employees that this type of conversation was not appropriate for hallway talk, it made me think of HIPAA, patient privacy, and protected health information. Several years ago, the mere mention of the term HIPAA led to insomnia as pharmacists tried to determine how to comply with the law's rules and regulations. Today, HIPAA is incorporated into how we practice pharmacy. But are we really doing what we should? Should there be insomnia about HIPAA?■Conduct patient privacy checks to catch any potential HIPAA issues.■Educate all staff, new and current, on patient privacy requirements. ■Conduct patient privacy checks to catch any potential HIPAA issues.■Educate all staff, new and current, on patient privacy requirements. HIPAA, the Health Insurance Portability and Accountability Act, was enacted in 1996. The first section of the act protected health insurance coverage for workers and their families when changing or losing a job. The second section required the establishment of national standards for electronic health care transactions and national identifiers for providers, health insurance plans, and employers. Also part of the second section were provisions on the security and privacy of health data. Many changes have occurred in pharmacy practice since 1996. Electronic health records are commonplace in hospitals or health systems. We have access to more information about our patients than ever before. In many hospitals, not only can we review the medical records of hospitalized patients, we can also review the medical records from their physician's practice. This information is very beneficial to critical clinical decisions and will become increasingly useful for continuity of care. But are we adequately guarding that protected health information? Hospitals typically provide training on HIPAA as part of new employee orientation and on an ongoing basis. From a pharmacist's perspective, patient information is necessary to provide care—but what about the other people working in the pharmacy? Depending on their role, that information may be necessary as well. For example, if a pharmacy technician is involved in gathering medications as part of medication reconciliation, then it may be very important for that individual to have access to a patient's outpatient record. However, if the pharmacy technician is responsible for preparing an inpatient's I.V. medications, do they need access to outpatient records? Access can be limited, but managers and department directors must keep close tabs on who has what access. Over time, people, jobs, and functions can change, and it is important to make sure that access changes as well. All employees must also remember that even if they need protected health information to do their job, it isn't to be shared with those who do not. Hallway conversations are simply not allowed. HIPAA is not new, but there are frequently new provisions or changes to the act, and its penalties and sanctions continue to evolve. No one wants to experience insomnia worrying about a potential HIPAA violation, and some simple steps can keep that from occurring. Conducting patient privacy checks may help you monitor what's happening in the pharmacy and identify any potential issues. Review pharmacy staff members' electronic access and ensure that it's appropriate. Last, provide pharmacy-specific education for all staff, but especially for new employees, to reinforce the importance of patient privacy.