Ahmed M. Elmisery

Privacy Preserving Distributed Learning Clustering of HealthCare Data Using Cryptography Protocols

In this paper, the analysis and design of single-to-balanced combline bandpass filter are presented. The cross-coupled effect is introduced to create two transmission zeros that can be located independently in either the upper or lower stopband. Notably, the effect of nonadjacent line coupling on the filter response is properly addressed, and an efficient way to compensate it is proposed. Also, the issue of a frequency-dependent J-inverter in bandpass filter design is also well treated. The proposed filter can be implemented using the low-temperature co-fired ceramic (LTCC) process to achieve very compact circuit size, in which the combline line lengths are as small as . Specifically, two third-order single-to-balanced combline bandpass filters in LTCC are implemented. Good balance performance, low loss, good selectivity, and compact size are achieved.Data mining is the process of knowledge discovery in databases (centralized or distributed); it consists of different tasks associated with them different algorithms. Nowadays the scenario of one centralized database that maintains all the data is difficult to achieve due to different reasons including physical, geographical restrictions and size of the data itself. One approach to solve this problem is distributed databases where different parities have horizontal or vertical partitions of the data. The data is normally maintained by more than one organization, each of which aims at keeping its information stored in the databases private, thus, privacy-preserving techniques and protocols are designed to perform data mining on distributed data when privacy is highly concerned. Cluster analysis is a frequently used data mining task which aims at decomposing or partitioning a usually multivariate data set into groups such that the data objects in one group are the most similar to each other. It has an important role in different fields such as bio-informatics, marketing, machine learning, limate and healthcare. In this paper we introduce a novel clustering algorithm that was designed with the goal of enabling a privacy preserving version of it, along with sub-protocols for secure computations, to handle the clustering of vertically partitioned data among different healthcare data providers.

Agent Based Middleware for Maintaining User Privacy in IPTV Recommender Services

Recommender services that are currently used by IPTV providers help customers to find suitable content according to their preferences and increase overall content sales. Such systems provide competitive advantage over other IPTV providers and improve the overall performance of the current systems by building up an overlay that increases content availability, prioritization and distribution that is based on users’ interests. Current implementations are mostly centralized recommender service (CRS) where the information about the users’ profiles is stored in a single server. This type of design poses a severe privacy hazard, since the users’ profiles are fully under the control of the CRS and the users have to fully trust the CRS to keep their profiles private. In this paper, we present our approach to build a private centralized recommender service (PCRS) using collaborative filtering techniques and an agent based middleware for private recommendations (AMPR). The AMPR ensures user profile privacy in the recommendation process. We introduce two obfuscation algorithms embedded in the AMPR that protect users’ profile privacy as well as preserve the aggregates in the dataset in order to maximize the usability of information for accurate recommendations. Using these algorithms provides the user complete control on the privacy of his personal profile. We also provide an IPTV network scenario that uses AMPR and its evaluations.

Agent based middleware for private data mashup in IPTV recommender services

Data mashup is a web technology that combines information from multiple sources into a single web application. Mashup applications create a new horizon for different services like real estate services, financial services and recommender services. Recommender systems are a serious business tool for the providers of IPTV services, who seek to gain competitive advantage over competing providers and attract more customers. An IPTV provider utilizes data mashup to merge datasets from different movie recommendation sites like Netflix or IMDb in order to leverage its recommender performance and predication accuracy. However, mashup different datasets from multiple sources is a privacy hazard as it might revels customer specific preferences for different items. The ability to preserve privacy in mashuped datasets and in the same time provide accurate recommendations becomes a key success for the spread of mashup services. In this paper, we present our efforts to build an agent based middleware for private data mashup (AMPM) to serve centralized IPTV recommender service (CIRS). AMPM is equipped with obfuscation mechanisms to preserve privacy of the merged datasets form multiple sources involved in the mashup application. Also these mechanisms preserve the aggregates in the dataset to maximize the usability of information in order to attain accurate recommendations. We also provide a data mashup scenario in IPTV recommender service and experimentation results.

Privacy Aware Obfuscation Middleware for Mobile Jukebox Recommender Services

Mobile Jukebox is a service offered by mobile operators to their clients, such that subscribers can buy or download anywhere, anytime full-length music tracks over the 3G Mobile networks. Unlike some music download services, the subscribers can reuse the selected tracks on their music players or computers. As the amount of online music grows rapidly, Jukebox providers employ automatic recommender service as an important tool for music listeners to find music that they will appreciate. On one hand, Jukebox recommender service recommend music based on users’ musical tastes and listening habits which reduces the browsing time for searching new songs and album releases. On the other hand, users care about the privacy of their preferences and individuals’ behaviors regarding the usage of recommender service. This work presents our efforts to design an agent based middle-ware that enables the end-user to use Jukebox recommender services without revealing his sensitive profile information to that service or any third party involved in this process. Our solution relies on a distributed multi-agent architecture involving local agents running on the end-user mobile phone and two stage obfuscation process used to conceal the local profiles of end-users with similar preferences. The first stage is done locally at the end user side but the second stage is done at remote nodes that can be donated by multiple non-colluding end users that requested the recommendations or third parties mash-up service. All the communications between participants are done through anonymised network to hide their network identity. In this paper, we also provide a mobile jukebox network scenario and experimentation results.

An Agent Based Middleware for Privacy Aware Recommender Systems in IPTV Networks

IPTV providers keen to use recommender systems as a serious business tool to gain competitive advantage over competing providers and attract more customers. As indicated in (Elmisery, Botvich 2011b) IPTV recommender systems can utilize data mashup to merge datasets from different movie recommendation sites like Netflix or IMDb to leverage its recommender performance and predication accuracy. Data mashup is a web technology that combines information from multiple sources into a single web application. Mashup applications created a new horizon for different services like real estate services, financial services and recommender systems. On the other hand, mashup applications bring about additional requirement related to the privacy of data used in the mashup process. Moreover, privacy and accuracy are two contradicting goals that need to be adjusted for the spread of these services. In this work, we present our efforts to build an agent based middleware for private data mashup (AMPM) that serve centralized IPTV recommender system (CIRS). AMPM is equipped with two obfuscation mechanisms to preserve privacy of the dataset collected from each provider involved in the mashup application. We present a model to measure privacy breaches. Also, we provide a data mashup scenario in IPTV recommender system and experimentation results.

Collaborative privacy framework for minimizing privacy risks in an IPTV social recommender service

In our connected world, recommender systems have become widely known for their ability to provide expert and personalized referrals to end-users in different domains. The rapid growth of social networks has given a rise to a new kind of systems, which have been termed “social recommender service”. In this context, a software as a service recommender system can be utilized to extract a set of suitable referrals for certain users based on the data collected from the personal profiles of other end-users within a social structure. However, preserving end-users privacy in social recommender services is a very challenging problem that might prevent privacy concerned users from releasing their own profiles’ data or to be forced to release an erroneous data. Thus, both cases can detain the accuracy of extracted referrals. So in order to gain accurate referrals, the social recommender service should have the ability to preserve the privacy of end-users registered in their system. In this paper, we present a middleware that runs on the end-users’ side in order to conceal their profiles data when being released for the recommendation purposes. The computation of recommendation proceeds over this concealed data. The proposed middleware is equipped with a distributed data collection protocol along with two stage concealment process to give the end-users complete control over the privacy of their profiles. We will present an IPTV network scenario along with the proposed middleware. A number of different experiments were performed on real data which was concealed using our two stage concealment process to evaluate the achieved privacy and accuracy of the extracted referrals. As supported by the experiments, the proposed framework maintains the recommendations accuracy with a reasonable privacy level.

Multi-agent based middleware for protecting privacy in IPTV content recommender services

This work presents our efforts to design an agent based middleware that enables the end-users to use IPTV content recommender services without revealing their sensitive preference data to the service provider or any third party involved in this process. The proposed middleware (called AMPR) preserves users’ privacy when using the recommender service and permits private sharing of data among different users in the network. The proposed solution relies on a distributed multi-agent architecture involving local agents running on the end-user set up box to implement a two stage concealment process based on user role in order to conceal the local preference data of end-users when they decide to participate in recommendation process. Moreover, AMPR allows the end-users to use P3P policies exchange language (APPEL) for specifying their privacy preferences for the data extracted from their profiles, while the recommender service uses platform for privacy preferences (P3P) policies for specifying their data usage practices. AMPR executes the first stage locally at the end user side but the second stage is done at remote nodes that can be donated by multiple non-colluding end users that we will call super-peers Elmisery and Botvich (2011a, b, c); or third parties mash-up service Elmisery A, Botvich (2011a, b). Participants submit their locally obfuscated profiles anonymously to their local super-peer who collect and mix these preference data from multiple participants. The super-peer invokes AMPR to perform global perturbation process on the aggregated preference data to ensure a complete concealment of user’s profiles. Then, it anonymously submits these aggregated profiles to a third party content recommender service to generate referrals without breaching participants’ privacy. In this paper, we also provide an IPTV network scenario and experimentation results. Our results and analysis shows that our two-stage concealment process not only protect the users’ privacy, but also can maintain the recommendation accuracy

A new computing environment for collective privacy protection from constrained healthcare devices to IoT cloud services

The Internet of healthcare things is essentially a new model that changes the way of the delivery and management of healthcare services. It utilizes digital sensors and cloud computing to present a quality healthcare service outside of the classical hospital environment. This resulted in the emergence of a new class of online web 4.0 services, which are termed “cloud healthcare services”. Cloud healthcare services offer a straightforward opportunity for patients to communicate with healthcare professionals and utilize their personal IoHT devices to obtain timely and accurate medical guidance and decisions. The personal IoHT devices integrate sensed health data at a central cloud healthcare service to extract useful health insights for wellness and preventive care strategies. However, the present practices for cloud healthcare services rely on a centralized approach, where patients’ health data are collected and stored on servers, located at remote locations, which might be functioning under data privacy laws somewhat different from the ones applied where the service is running. Promoting a privacy respecting cloud services encourages patients to actively participate in these healthcare services and to routinely provide an accurate and precious health data about themselves. With the emergence of fog computing paradigm, privacy protection can now be enforced at the edge of the patient’s network regardless of the location of service providers. In this paper, a framework for cloud healthcare recommender service is presented. We depicted the personal gateways at the patients’ side act as intermediate nodes (called fog nodes) between IoHT devices and cloud healthcare services. A fog-based middleware will be hosted on these fog nodes for an efficient aggregation of patients generated health data while maintaining the privacy and the confidentiality of their health profiles. The proposed middleware executes a two-stage concealment process that utilizes the hierarchical nature of IoHT devices. This will unburden the constrained IoHT devices from performing intensive privacy preserving processes. At that, the patients will be empowered with a tool to control the privacy of their health data by enabling them to release their health data in a concealed form. The further processing at the cloud healthcare service continues over the concealed data by applying the proposed protocols. The proposed solution was integrated into a scenario related to preserving the privacy of the patients’ health data when utilized by a cloud healthcare recommender service to generate health insights. Our approach induces a straightforward solution with accurate results, which are beneficial to both patients and service providers.

Enhanced Middleware for Collaborative Privacy in Community Based Recommendations Services

Recommending communities in social networks is the problem of detecting, for each member, its membership to one of more communities of other members, where members in each community share some relevant features which guaranteeing that the community as a whole satisfies some desired properties of similarity. As a result, forming these communities requires the availability of personal data from different participants. This is a requirement not only for these services but also the landscape of the Web 2.0 itself with all its versatile services heavily relies on the disclosure of private user information. As the more service providers collect personal data about their customers, the growing privacy threats pose for their patrons. Addressing end-user concerns privacy-enhancing techniques (PETs) have emerged to enable them to improve the control over their personal data. In this paper, we introduce a collaborative privacy middleware (EMCP) that runs in attendees’ mobile phones and allows exchanging of their information in order to facilities recommending and creating communities without disclosing their preferences to other parties. We also provide a scenario for community based recommender service for conferences and experimentation results.

Secure e-Payment using Multi-agent Architecture

This paper investigates how multi-agents can digitally sign transactions in an untrusted environment securely. We present an agent-based scenario for e-Payment and discuss techniques using multiple agents that have been implemented to provide security in this scenario. The underlying techniques are based on existing theoretical schemes. These schemes generally have a significant overhead which is caused by the use of multiple agents. However, this overhead can be compensated by the functionality offered in our proposed scenario. The schemes require no interaction of the originator once the mobile agents are sent out. They are therefore particularly suited for situations in which a user cannot stay online for a long time. We also address some possible security threats which lead to new observations on using threshold signature schemes in the context of mobile agents