Generating Private Recommendations Efficiently Using Homomorphic Encryption and Data Packing

Erkin, Zekeriya; Veugen, Thijs; Toft, Tomas; Lagendijk, Reginald L.

doi:10.1109/tifs.2012.2190726

Cited by 184 publications

(126 citation statements)

References 23 publications

Supporting

Mentioning

119

Contrasting

Order By: Relevance

“…Amid less measure of time client ought to get more evaluated item. Documentation and Security Model: In paper [1], creator consider just the semi fair model where members are thought to be straightforward yet inquisitive. Along these lines, the protection saving convention they exhibit in this paper can be demonstrated secure in this model by utilizing existing evidences for the fundamental building pieces, which it is made out of.…”

Section: Approachesmentioning

confidence: 99%

Commendation Generation by Homomorphic Encryption

Prajapati¹

2017

International Journal of Recent Advancement in Engineering &Amp; Research

View full text Add to dashboard Cite

Abstract-The Online prescribed framework empower individual administration to client. We are concentrating on system that works on security to touchy client information, which can be adjusted by the administration supplier. This will secure client information by encryption of the information and executes proposals by the encryption. In this way, the administration supplier watches neither client inclinations nor proposals. The technique proposed by creator utilizes homomorphic encryption and secures multi-party calculation, which present a noteworthy overhead in computational many-sided quality. Creator minimize the presented overhead by securing information and utilizing cryptographic conventions especially produced for this reason.I. INTRODUCTION From many years, we have experienced phenomenal progress in information and communication systems. As a result, online applications have become very popular for millions of people. Personalization is a common approach to attract even more people to web service. The system can suggest personalized services tailored to a particular user based on his preferences. Since the personalization of the services offers high profits to the service providers and poses interesting research challenges, research for generating recommendations, which interests from industry. The techniques for generating recommendations for users strongly rely on the way personal user information is gathered. This information can be provided by the user himself as in profiles, or the service provider can observe users' actions like click logs. On one hand, more user information helps the system to improve the accuracy of the recommendations. On the other hand, the personal information on the users creates a severe privacy risk since there is no solid guarantee for the service provider not to misuse the users' data. It is often seen that whenever a user login in the system, service providers statements the ownership of data provided from user and approves itself to allocate the data to third party. To address the privacy considerations in recommendation systems, in, Canny proposes a system where the private user data is encrypted and recommendations are generated by applying an iterative procedure based on the conjugate gradient. This algorithm calculates characterization of user in subspace. It generates recommendation by computing projections in encrypted domain. This iterative algorithm takes many rounds for convergence and in each round; users need to participate in an expensive decryption procedure which is based on a threshold value, where an important portion of the users is assumed to be online and honest. Private recommendation system uses elgamel algorithm but system is more complex and inefficient. To overcome this drawback proposed system uses ElGamal algorithm. This system is efficient to generate private recommendations in a privacy-preserving manner. II. LITERATURE SURVEYAs of late, outsourcing vast measure of information in framework and how to deal with the information raises numerous ...

show abstract

Section: Approachesmentioning

confidence: 99%

Commendation Generation by Homomorphic Encryption

Prajapati¹

2017

International Journal of Recent Advancement in Engineering &Amp; Research

View full text Add to dashboard Cite

show abstract

“…For example, two illustrative works [23,24] propose the use of homomorphic encryption as a structural element for their systems. Another system that uses randomized perturbation instead of encryption has been proposed in [25].…”

Section: Privacy-enhanced Recommender Systemsmentioning

confidence: 99%

A Privacy-by-Design Contextual Suggestion System for Tourism

Efraimidis

Drosatos

Arampatzis

et al. 2016

JSAN

View full text Add to dashboard Cite

Abstract:We focus on personal data generated by the sensors and through the everyday usage of smart devices and take advantage of these data to build a non-invasive contextual suggestion system for tourism. The system, which we call Pythia, exploits the computational capabilities of modern smart devices to offer high quality personalized POI (point of interest) recommendations. To protect user privacy, we apply a privacy by design approach within all of the steps of creating Pythia. The outcome is a system that comprises important architectural and operational innovations. The system is designed to process sensitive personal data, such as location traces, browsing history and web searches (query logs), to automatically infer user preferences and build corresponding POI-based user profiles. These profiles are then used by a contextual suggestion engine to anticipate user choices and make POI recommendations for tourists. Privacy leaks are minimized by implementing an important part of the system functionality at the user side, either as a mobile app or as a client-side web application, and by taking additional precautions, like data generalization, wherever necessary. As a proof of concept, we present a prototype that implements the aforementioned mechanisms on the Android platform accompanied with certain web applications. Even though the current prototype focuses only on location data, the results from the evaluation of the contextual suggestion algorithms and the user experience feedback from volunteers who used the prototype are very positive.

show abstract

“…For instance, medical data can have any link with patients' identification removed before being available for research. Privacy is usually obtained by applying Privacy Enhancing Technologies (PETs) [11][12][13][14]. PETs alone do not ensure transparency, again because they are not designed to address regulation requirements stating that users must be informed.…”

Section: Transparency For Security and Privacymentioning

confidence: 99%

Can Transparency Enhancing Tools Support Patient’s Accessing Electronic Health Records?

Ferreira

Lenzini

2015

New Contributions in Information Systems and Technologies

View full text Add to dashboard Cite

Abstract. Patients that access their health records take more care of their health and, when in therapy, commit more seriously to improve their condition. This leads to a more effective and more efficient healthcare management, and is also in agreement with European directives on data protection. However, accessing medical data can be risky. Security should be assured and it should be evident to the patients, who has access to what data and any violation to patient's privacy requirements should be reported. We call this property transparency. Precisely this work looks into the Transparency Enhancing Tools that have been proposed to increase people's awareness about security and privacy on the Internet, and discusses to which extent these tools can empower transparency in healthcare.

show abstract

Generating Private Recommendations Efficiently Using Homomorphic Encryption and Data Packing

Cited by 184 publications

References 23 publications

Commendation Generation by Homomorphic Encryption

Commendation Generation by Homomorphic Encryption

A Privacy-by-Design Contextual Suggestion System for Tourism

Can Transparency Enhancing Tools Support Patient’s Accessing Electronic Health Records?

Contact Info

Product

Resources

About