Sanitization Mechanism Research Articles

SANITIZATION SYSTEM FOR HOSPITALS

The paper introduces a robot equipped with UV light and spray sanitizer for cleaning surfaces and objects. It's designed to combat the spread of pathogens by sanitizing various environments, with a focus on hospitals, especially ICU and operation theatres. Its dual sanitization mechanisms, UV light and spray sanitizer dispenser provides optimal efficiency. This combination ensures thorough cleaning and disinfection of diverse surfaces, enhancing public health and safety. The robot's versatility makes it suitable for use in hospitals, transportation systems, educational institutions, and other high-traffic areas where sanitation is crucial. By integrating UV sanitization technology with a spray sanitizer mechanism, the robot offers comprehensive coverage against infectious diseases, providing an effective solution for maintaining cleanliness and hygiene in various settings. Overall, the robot's autonomous operation and remote monitoring capabilities make it a valuable tool for promoting public health and mitigating the spread of harmful pathogens. Key Words: sanitization, robot, UV light, spray sanitizer, pathogen control.

Application of Slightly Acidic Electrolyzed Water as a Potential Sanitizer in the Food Industry

The food industry has extensively explored postharvest microbial control, seeking viable technologies to ensure food safety. Although numerous chlorine-based commercial sanitizers serve this purpose, many are plagued by constraints such as instability and diminished disinfectant efficacy. These issues arise from exposure to organic matter in wash water, light, or air. As an innovative and promising alternative, slightly acidic electrolyzed water (SAEW) has emerged, captivating attention for its robust sterilization potential and eco-friendliness in agricultural and food sectors. SAEW generated via electrolysis of a diluted hydrochloric acid (HCl) solution with concentrations ranging from 2 to 6% or aqueous solution of sodium chloride (NaCl) in a nonmembrane electrolytic chamber is reported to possess equivalent antimicrobial properties as strong acidic electrolyzed water (StAEW). In contrast to traditional chlorine sanitizers, SAEW leaves less chlorine residue on sanitized foods such fresh-cut fruit and vegetables, meat, poultry, and aquatic products due to its low available chlorine concentration (ACC). Its near neutral pH of 5 to 6.5 not only renders it environmentally benign but also mitigates the production of chlorine gas, a contrast to low pH conditions seen in StAEW generation. The bactericidal effect of SAEW against various strains of foodborne pathogens is widely believed and accepted to be due to the combined action of high oxidation-reduction-potential (ORP) reactions and undissociated hypochlorite/hypochlorous acid (HOCl). Consequently, a burgeoning interest surrounds the potential of SAEW for sanitation in the food industry, offering an alternative to address shortcomings in sodium hypochlorite solutions and even StAEW. It has been hypothesized from a number of studies that SAEW treatment can increase the quality and nutritional value of harvested fruits, which in turn may enhance their ability to be stored. Therefore, SAEW is not only a promising sanitizer in the food industry but also has the potential to be an efficient strategy for encouraging the accumulation of bioactive chemicals in plants, especially if it is used extensively. This review encapsulates the latest insights concerning SAEW, encompassing its antimicrobial effectiveness, sanitization mechanism, advantages vis-à-vis other sanitizers, and plausible applications across the food industry.

Graph rewriting primitives for semantic graph databases sanitization

Due to the rapid proliferation of data online, an important quantity of private or sensitive informations is being stored as linked data in graph databases (e.g., represented as RDF). For such databases to be shared without jeopardizing privacy, they must first undergo a process known as database sanitization. During this process, databases are transformed following graph transformations that are usually described informally or through ad-hoc processes. However, a more thourough formalization of these transformations would aid in analysing the sanitization process, ensuring its correctness, and demonstrating the resulting privacy guarantees. This paper is an effort toward bridging the gap between the rigorous graph rewriting approaches and graph sanitization. We propose a graph transformation language to serve as a basis for constructing various sanitization mechanisms. This language relies on a set of elementary transformation operators formalized using a generic algebraic graph rewriting approach. Our language takes into account semantic and supports the equivalent of WHERE and EXCEPT clauses. As a proof of concept, we use these operators to implement two mechanisms from the literature, one generic (Local Differential Privacy) and one specifically introduced for semantic graph databases (sensitive attribute masking through anatomization). We propose an open-sourced tool implementing the elementary operators and the privacy mechanisms we derive from them relying on the Attributed Graph Grammar System (AGG) and its java API, providing a concrete tool implementing formal graph rewriting mechanisms to sanitize semantic graph databases. We present experimental results on this implementation regarding both proposed schemes and discuss its efficiency and scalability.

Highly secured EHR management system based on blockchain technology with digitally signed authentication using data sanitization and polynomial interpolation

With the increased specifications of healthcare services, diagnosis become very common in most of the healthcare sector to provide better treatment for patients. Here, the person’s medical information can be tracked with the help of an Electronic Health Record (EHR). The EHR system maintains the digital medical records of medical organizations. Enterprise-wide data systems, along with other networking activities, are used to exchange medical documents in the current environment where the patient seeks rapid access to their medical records. Moreover, the EHR system faces many problems regarding integrity, management and security. Still, there are some concerns regarding the security and privacy of patient medical records. However, it suffers from managing the large volume of electronically generated healthcare data. Thus, this work adapts blockchain technology, which effectively stores healthcare data. Therefore, decentralized technology was just recently introduced in order to offer an innovative viewpoint on data security and system effectiveness. In this work, a blockchain-related system is developed to secure patients’ health data with high verifiability. A total of two mechanisms are carried out in the developed model to encrypt the privacy of the health data that is Data Sanitization and Polynomial Interpolation-based Cryptography (DSPIC). In the data sanitization mechanism, the optimal key is generated through the Modernized Position-based Coot and Penguins Search Optimization Algorithm (MP-CPeSOA). The polynomial interpolation method is carried out through the estimation of values between two data points to handle the heterogeneity. The objective function is considered for securing the patient's health data with Euclidian distance, hiding ratio, preservation ratio and correlation among the original data and the restored data. In this developed model, the generated key is digitally signed to provide high security for the patient’s health data. At last, the effectiveness of the suggested blockchain-adopted EHR preservation system is enhanced by conducting experiments on different EHR management schemes.

Evaluation of a novel water-soluble decanoic acid formulation as a fruit sanitizer

Fruits irrigated with contaminated water can transmit various pathogens. High sugar content in fruits such as black cherry (BC) fruit encourages microbial proliferation. A novel water-soluble decanoic acid (WSDA) was evaluated as a fruit sanitizer and compared with other traditional fruit sanitizers such as ethanol, bleach, or dishwasher surfactants. WSDA sanitizer killed yeasts, molds and bacteria including E. coli microbes effectively as other sanitizers with (4 log cycle reduction) of microbial load. Furthermore, the bacterial sanitization mechanism i.e. bactericidal or bacteriostatic was evaluated for alcohol, bleaching and WASDA solutions. E. coli was selected as the model pathogen used for such comparison. Results indicated that the mechanism of action for the three sanitizer solutions against E. coli was bactericidal. The problem with most used fruit sanitizers is their negative influence on fruit quality in terms of physical, mechanical and taste properties. In addition, some led to toxicological and ecological concerns. Thus, studies were conducted to explore the changes in the exocarp cell structure of BC fruit upon exposure to WSDA and other sanitizers using microscopic investigation. WSDA could have a very mild or gentle effect on the BC fruit cells compared to other sanitizers. Alcohol, bleaching and dishwasher surfactant changed the cellular structures and the intercellular spaces. Sanitizers may also affect fruit swelling. WSDA showed an increase in percent weight gain but it was significantly (p < 0.05) much lower than dishwasher surfactant and bleaching solution. BC Fruit flesh firmness and hardness were investigated upon exposure to different sanitizer solutions. BC fruit treated with WSDA showed the highest firmness values. Some liquid sanitizers could affect fruit quality in terms of fruit taste. Sensory evaluation in terms of the sanitizer's smell, texture and hedonic of BC fruit after soaking in different sanitizers was carried out. All sensory parameters of BC fruit soaked with WSDA were similar with insignificant differences (p > 0.05) compared to BC fruit soaked in tap water. However, the sensory parameters were significantly different (p < 0.05) when compared with alcohol, bleach and dishwasher surfactant. This ensures that WSDA was superior to other evaluated sanitizers in terms of physical, mechanical and fruit quality.

Statistical Data Privacy: A Song of Privacy and Utility

To quantify trade-offs between increasing demand for open data sharing and concerns about sensitive information disclosure, statistical data privacy (SDP) methodology analyzes data release mechanisms that sanitize outputs based on confidential data. Two dominant frameworks exist: statistical disclosure control (SDC) and the more recent differential privacy (DP). Despite framing differences, both SDC and DP share the same statistical problems at their core. For inference problems, either we may design optimal release mechanisms and associated estimators that satisfy bounds on disclosure risk measures, or we may adjust existing sanitized output to create new statistically valid and optimal estimators. Regardless of design or adjustment, in evaluating risk and utility, valid statistical inferences from mechanism outputs require uncertainty quantification that accounts for the effect of the sanitization mechanism that introduces bias and/or variance. In this review, we discuss the statistical foundations common to both SDC and DP, highlight major developments in SDP, and present exciting open research problems in private inference.

Quantifying the Effects of Anonymization Techniques Over Micro-Databases

Micro-databases are unique datasets that contain person-specific information about individuals. Preserving the privacy of such datasets has become a cause for serious concern since this massive repository of personalized data regularly gets published in the public domain. Sanitization mechanisms are specialized techniques that provide the required privacy guarantees to the published data. The work in this article establishes an efficient framework for quantitatively estimating the effectiveness of any privacy-preservation scheme which employs the anonymization principle. In our study, we have introduced an information-theoretic metric termed as <i>Sanitization Degree</i> <inline-formula><tex-math notation="LaTeX">$(\eta)$</tex-math></inline-formula> which assigns a cumulative score in the range [0,1] for a generic anonymization process. The design of our proposed metric is based on the fundamental fact that any sanitization mechanism attempts to reduce the amount of correlated information within the database attributes while simultaneously preserving the utility of the original dataset. Furthermore, we have characterized the <i>privacy-utility</i> tradeoff associated with our model by establishing a working relationship between these two fundamental quantities. We have empirically computed the value of <inline-formula><tex-math notation="LaTeX">$\eta$</tex-math></inline-formula> for three popular anonymization models ( <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -anonymity, <inline-formula><tex-math notation="LaTeX">$l$</tex-math></inline-formula> -diversity, and <inline-formula><tex-math notation="LaTeX">$t$</tex-math></inline-formula> -closeness) over a couple of real-life micro-databases, thereby demonstrating the practicability of our study.

A novel privacy-aware model for nonparametric decentralized detection

In recent years, the increasing development of the Internet of Things (IoT) demands the enhancement of both network security and user privacy protection. In the decentralized IoT network, multiple sensors send local observations to a fusion center for data aggregation and authorized hypothesis detection. But at the same time, private information might be inferred illegally, which would cause privacy leakage. In this paper, a novel privacy-aware model named AL-UP is proposed for the nonparametric decentralized detection in the IoT network. It aims to design a local differential privacy and data projection based sanitization mechanism for sensors, to hide the sensitive information in raw observations and protect the data and inference privacy. Based on the adversarial learning framework and linear discriminant analysis, we propose a max-min optimization problem to design parameters of the sanitization mechanism and hypothesis detection rules. The problem is solved via the block coordinate descend method. Numerical results on various public datasets indicate that the proposed model achieves better utility-privacy trade-off than the state of the arts.

A Graph-Based Differentially Private Algorithm for Mining Frequent Sequential Patterns

Currently, individuals leave a digital trace of their activities when they use their smartphones, social media, mobile apps, credit card payments, Internet surfing profile, etc. These digital activities hide intrinsic usage patterns, which can be extracted using sequential pattern algorithms. Sequential pattern mining is a promising approach for discovering temporal regularities in huge and heterogeneous databases. These sequences represent individuals’ common behavior and could contain sensitive information. Thus, sequential patterns should be sanitized to preserve individuals’ privacy. Hence, many algorithms have been proposed to accomplish this task. However, these techniques add noise to the candidate support before they are validated as, frequently, and thus, they cannot be applied without having access to all the users’ sequences data. In this paper, we propose a differential privacy graph-based technique for publishing frequent sequential patterns. It is applied at the post-processing stage; hence it may be used to protect frequent sequential patterns after they have been extracted, without the need to access all the users’ sequences. To validate our proposal, we performed a detailed assessment of its utility as a pattern mining algorithm and calculated the impact of the sanitization mechanism on a recommender system. We further evaluated its information loss disclosure risk and performed a comparison with the DP-FSM algorithm.

Design and Simulation of a Service Robot for Covid-19 Isolation Wards with Autonomous Navigation

The robots that are used to carry out the daily repetitive tasks in any type of environments are service robots. These robots have a great potential to eliminate human intervention and reduce contact in order to decrease the spread of virus. Service robots are of great importance when the human society is hit with a situation of pandemic as they can help in serving the patients in and infectious environment and isolating them from the surroundings. Such kind of a service robot that would serve infected patients in isolation wards is designed &amp; simulated by taking in the consideration the requirements. The dimensions of robot such as to optimize workspace requirements and the simulation of position and task of supplying the deliverables to patients from buffer area to isolation area are presented. The designed robot can successfully deliver medicines, food etc.to the patients without any contact between doctors, nurses or patients by using an on boarded Radio Frequency Identification system. The robot is well equipped with the sanitization mechanism to disinfect itself.

File Guard: automatic format-based media file sanitization

This paper proposes a format-based file sanitization mechanism, File Guard, aiming at preventing software vulnerabilities from being triggered by input files. Based on our experiments and the statistics on Common Vulnerabilities and Exposures, we observed that most of the software vulnerabilities are exploited by malformed input files which violate their corresponding format standards. Hence, repairing the input file according to the format standard is effective in preventing the vulnerabilities from being exploited. In this work, we focus on media file types, such as image, audio, video. File Guard automatically sanitizes input files of known formats, disabling the files from triggering potential vulnerabilities in a target system (or a group of systems) with minimum data loss. Existing intrusion prevention systems and anti-virus tools typically compare the input file or executable with the ones which are known to be malicious, and eliminate such data or software before they get into the protected system. Instead of blocking the malicious or damaged input file, our mechanism repairs the data and provides a more user-friendly protection. File Guard can ensure that the input file meets its standard format and thus is not exploitable even by zero-day vulnerabilities.

Privacy-preserving Wi-Fi Analytics

Abstract As communications-enabled devices are becoming more ubiquitous, it becomes easier to track the movements of individuals through the radio signals broadcasted by their devices. Thus, while there is a strong interest for physical analytics platforms to leverage this information for many purposes, this tracking also threatens the privacy of individuals. To solve this issue, we propose a privacy-preserving solution for collecting aggregate mobility patterns while satisfying the strong guarantee of ε-differential privacy. More precisely, we introduce a sanitization mechanism for efficient, privacy-preserving and non-interactive approximate distinct counting for physical analytics based on perturbed Bloom filters called Pan-Private BLIP. We also extend and generalize previous approaches for estimating distinct count of events and joint events (i.e., intersection and more generally t-out-of-n cardinalities). Finally, we evaluate expirementally our approach and compare it to previous ones on real datasets.

De-anonymization attack on geolocated data

With the advent of GPS-equipped devices, a massive amount of location data is being collected, raising the issue of the privacy risks incurred by the individuals whose movements are recorded. In this work, we focus on a specific inference attack called the de-anonymization attack, by which an adversary tries to infer the identity of a particular individual behind a set of mobility traces. More specifically, we propose an implementation of this attack based on a mobility model called Mobility Markov Chain (MMC). An MMC is built out from the mobility traces observed during the training phase and is used to perform the attack during the testing phase. We design several distance metrics quantifying the closeness between two MMCs and combine these distances to build de-anonymizers that can re-identify users. Experiments conducted on real datasets demonstrate that the attack is both accurate and resilient to sanitization mechanisms.

Minimizing the disclosure risk of semantic correlations in document sanitization

Text sanitization is crucial to enable privacy-preserving declassification of confidential documents. Moreover, considering the advent of new information sharing technologies that enable the daily publication of thousands of textual documents, automatic and semi-automatic sanitization methods are needed. Even though several of these methods have been proposed, most of them detect and sanitize sensitive terms (e.g., people names, addresses, diseases, etc.) independently, neglecting the importance of semantic correlations. From the attacker’s perspective, semantic correlations can be exploited to disclose a sanitized term from the presence of one or several non-sanitized words. To tackle this problem, this paper presents a general-purpose method that, by taking the output of a standard sanitization mechanism, analyses, detects and proposes for sanitization those semantically correlated terms that represent a plausible disclosure risk for the already sanitized ones. Our method relies on an information-theoretic formulation of disclosure risk which is able to adapt its behavior to the criterion of the initial sanitizer. The evaluation, carried on over a collection of real documents, shows that semantic correlations represent a real privacy threat in prior sanitized documents, and that our method is able to detect them effectively. As a result, the disclosure risk of the sanitized output is significantly reduced with respect to standard sanitization mechanisms.

Duplication with Trapdoor Sensitive Attribute Values: A New Approach for Privacy Preserving Data Publishing

Privacy Preserving Data Publishing addresses the problem of publishing the data collected from data owners by the data holder or publisher such that personal sensitive information of the individual is preserved and the published data is highly useful. The anonymization techniques such as Generalization, Suppression, Swapping, Bucketization and Randomization suffers from either individual identity disclosure or the significant loss in information which reduces the usefulness of the data. In this paper, we present a novel Privacy Preserving Data Publishing scheme based on tuple duplication. We introduce the notion of Semantically Equivalent Attribute Values for sensitive attributes and Reputation Loss by Disclosure to hide the sensitive information of an individual in the published data. The Trapdoor Attribute Values for sensitive attributes are defined which helps in recovering the original dataset from the published dataset. We evaluate the proposed scheme with the existing attack models and show that our scheme counters those attacks. We define our own privacy criterion and show that the published data achieves the same. We assess the utility of the published data by using the existing utility metrics and our own defined utility metric. We show that the utility of the published data using the proposed sanitization mechanism is high.