Survey Paper on Generating Customer Relationship Management Efficiently using Homomorphic Encryption and Data Packing

In recommender systems, recommendations are generated based on the data collected from the user. The important requirement of the basic Information Filtering architectures is to protect the privacy of all the users. By using the Homomorphic encryption and data packing the recommender system provides good privacy of customer data. The data protection system gives security from malicious third parties, but does not provide security from the service provider. In this paper, our aim is to generate the dynamic recommendations and protect the confidential data of user against the service provider while protecting the functionality of the system. This system is very useful to generate dynamic recommendations by preserving the privacy of the users

Generating Private Recommendation System Using Multiple Homomorphic Encryption Scheme

The recommender system is important tool in online application to generate the recommendation services. Recommendations are generated by collecting the data from users need; online services access the user’s profiles for generating useful recommendations. Privacy sensitive data is used for to collect the data. Collaborative filtering technique gives privacy for sensitive data if data is misused by other service providers or leaked. Existing system uses Paillier encryption algorithm & DGK algorithm to secure user data from malicious third party as well as to protect the private data against service provider but system is more complex and inefficient. Proposed system protects the privacy of user using encrypting the sensitive data. The system uses multiple homomorphic algorithms to secure user data from service providers. The system is used to protect the confidential data of user against the service provider while providing online services. Encrypting private data is recommended and process on data to generate recommendations. To construct efficient system that does not require the active participation of the user. The experiment shows that the result that provide the security by hiding the personal data of user from third party DOI: 10.17762/ijritcc2321-8169.15076

Confidential Boosting with Random Linear Classifiers for Outsourced User-generated Data

User-generated data is crucial to predictive modeling in many applications. With a web/mobile/wearable interface, a data owner can continuously record data generated by distributed users and build various predictive models from the data to improve their operations, services, and revenue. Due to the large size and evolving nature of users data, data owners may rely on public cloud service providers (Cloud) for storage and computation scalability. Exposing sensitive user-generated data and advanced analytic models to Cloud raises privacy concerns. We present a confidential learning framework, SecureBoost, for data owners that want to learn predictive models from aggregated user-generated data but offload the storage and computational burden to Cloud without having to worry about protecting the sensitive data. SecureBoost allows users to submit encrypted or randomly masked data to designated Cloud directly. Our framework utilizes random linear classifiers (RLCs) as the base classifiers in the boosting framework to dramatically simplify the design of the proposed confidential boosting protocols, yet still preserve the model quality. A Cryptographic Service Provider (CSP) is used to assist the Cloud's processing, reducing the complexity of the protocol constructions. We present two constructions of SecureBoost: HE+GC and SecSh+GC, using combinations of homomorphic encryption, garbled circuits, and random masking to achieve both security and efficiency. For a boosted model, Cloud learns only the RLCs and the CSP learns only the weights of the RLCs. Finally, the data owner collects the two parts to get the complete model. We conduct extensive experiments to understand the quality of the RLC-based boosting and the cost distribution of the constructions. Our results show that SecureBoost can efficiently learn high-quality boosting models from protected user-generated data

Privacy preserving recommender systems

The recommender systems help users find suitable and interesting products and contents from the huge amount of information that are available in the internet. There are various types of recommender systems available which have been providing recommendation services to users. For example Collaborative Filtering (CF) based recommendations, Content based (CB) recommendations, context aware recommendations and so on. Despite the fact that these recommender systems are very useful to solve the information overload problem by filtering interesting information, they suffer from huge privacy issues. In order to generate user personalized recommendations, the recommendation service providers need to acquire the information related to attributes, preferences, experiences as well as demands, which are related to users' confidential information. Usually the more information available to the service providers, the more accurate recommendations can be generated. However, the service providers are not always trustworthy to share personal information for recommendation purposes since they may cause serious privacy threats to users' privacy by leaking them to other parties or providing false recommendations. Therefore the user information must be protected prior to share them to any third party service provider to ensure the privacy of users. To overcome the privacy issues of recommender systems several techniques have been proposed which can be categorized into decentralization, randomization and secure computations based approaches. In decentralization based approach, the central service providers are removed and the main controls of recommendation services are given to participant users. The main issue with this kind of approach is that to generate recommendations, the users need to be dependant to other users' availability in online services. If any user becomes offline, her information can not be used in the system. The randomization based techniques add noises to users data to obfuscate them from learning the true information. However the main issue is that adding noise affects recommendation accuracy. On the contrary, the secure computations preserve user information while providing accurate recommendations. In this thesis we preserve user privacy by means of encrypting user information, specifically their ratings and other related information using homomorphic encryption based techniques to provide recommendations based on the encrypted data. The main advantage of homomorphic encryption based technique is that it is semantically secure and computationally it is hard to distinguish the true information from the given ciphertext. Using the homomorphic based encryption tools and techniques we build different privacy preserving protocols for different types of recommendation approaches by analyzing their privacy requirements and challenges. More specifically, we focus on different key recommendation techniques and differentiate them into centralized and partitioned dataset based recommendation techniques. From available recommendation techniques, we found that some of the existing and popular recommendation techniques like user based recommendation, item based recommendation and context aware recommendation can be grouped into centralized recommendation approach. In partitioned dataset based recommendation, the user information can be partitioned into different organizations and these organizations can collaborate with each other by gathering sufficient information in order to provide accurate recommendations without revealing their own confidential information. After categorizing the recommendation techniques we analyze the problems and requirements in terms of privacy preservation. Then for each type of recommendation approach, we develop the privacy preserving protocols to generate recommendations taking their specific privacy requirements and challenges into consideration. We also investigate the problems and limitations of existing privacy preserving recommendations and found that the current solutions suffer from huge computation and communication overhead as well as privacy of users. In the thesis we identify the related problems and solve the issues using our proposed privacy preserving protocols. As an overall idea, our proposed recommendation protocols work as follows. The users encrypt their ratings using homomorphic encryption and send them to service providers. We assume the service providers are semi honest but curious, they follow the protocol but at the same time try to find new information from the available data. The service provider has the ability to perform homomorphic operations and it performs certain computations over encrypted data without learning any true information and returns the results to the query users who ask for recommendations. The system models of our privacy preserving protocols for different recommendation techniques differ from each other because of their different privacy requirements. The proposed privacy preserving protocols are tested on various real world datasets. Based on the application areas of different recommendation approaches our gathered datasets are also different such as movie rating, social network, checkin information for different locations and quality of service of web services. For each proposed privacy preserving protocols we also present the privacy analysis and describe how the system can perform the computations without leaking the private information of users. The experimental and privacy analysis of our proposed privacy preserving protocols for different types of recommendation techniques show that they are private as well as practical

Toward practical and private online services

Today's common online services (social networks, media streaming, messaging, email, etc.) bring convenience. However, these services are susceptible to privacy leaks. Certainly, email snooping by rogue employees, email server hacks, and accidental disclosures of user ratings for movies are some sources of private information leakage. This dissertation investigates the following question: Can we build systems that (a) provide strong privacy guarantees to the users, (b) are consistent with existing commercial and policy regimes, and (c) are affordable? Satisfying all three requirements simultaneously is challenging, as providing strong privacy guarantees usually necessitates either sacrificing functionality, incurring high resource costs, or both. Indeed, there are powerful cryptographic protocols---private information retrieval (PIR), and secure two-party computation (2PC)---that provide strong guarantees but are orders of magnitude more expensive than their non-private counterparts. This dissertation takes these protocols as a starting point and then substantially reduces their costs by tailoring them using application-specific properties. It presents two systems, Popcorn and Pretzel, built on this design ethos. Popcorn is a Netflix-like media delivery system, that provably hides, even from the content distributor (for example, Netflix), which movie a user is watching. Popcorn tailors PIR protocols to the media domain. It amortizes the server-side overhead of PIR by batching requests from the large number of concurrent users retrieving content at any given time; and, it forms large batches without introducing playback delays by leveraging the properties of media streaming. Popcorn is consistent with the prevailing commercial regime (copyrights, etc.), and its per-request dollar cost is 3.87 times that of a non-private system. The other system described in this dissertation, Pretzel, is an email system that encrypts emails end-to-end between senders and intended recipients, but allows the email service provider to perform content-based spam filtering and targeted advertising. Pretzel refines a 2PC protocol. It reduces the resource consumption of the protocol by replacing the underlying encryption scheme with a more efficient one, applying a packing technique to conserve invocations of the encryption algorithm, and pruning the inputs to the protocol. Pretzel's costs, versus a legacy non-private implementation, are estimated to be up to 5.4 times for the email provider, with additional but modest client-side requirements. Popcorn and Pretzel have fundamental connections. For instance, the cryptographic protocols in both systems securely compute vector-matrix products. However, we observe that differences in the vector and matrix dimensions lead to different system designs. Ultimately, both systems represent a potentially appealing compromise: sacrifice some functionality to build in strong privacy properties at affordable costs.Computer Science

Privacy-preserving recommendation system using federated learning

Federated Learning is a form of distributed learning which leverages edge devices for training. It aims to preserve privacy by communicating users’ learning parameters and gradient updates to the global server during the training while keeping the actual data on the users’ devices. The training on global server is performed on these parameters instead of user data directly while fine tuning of the model can be done on client’s devices locally. However, federated learning is not without its shortcomings and in this thesis, we present an overview of the learning paradigm and propose a new federated recommender system framework that utilizes homomorphic encryption. This results in a slight decrease in accuracy metrics but leads to greatly increased user-privacy. We also show that performing computations on encrypted gradients barely affects the recommendation performance while ensuring a more secure means of communicating user gradients to and from the global server

A Novel Privacy-Preserved Recommender System Framework based on Federated Learning

Recommender System (RS) is currently an effective way to solve information overload. To meet users' next click behavior, RS needs to collect users' personal information and behavior to achieve a comprehensive and profound user preference perception. However, these centrally collected data are privacy-sensitive, and any leakage may cause severe problems to both users and service providers. This paper proposed a novel privacy-preserved recommender system framework (PPRSF), through the application of federated learning paradigm, to enable the recommendation algorithm to be trained and carry out inference without centrally collecting users' private data. The PPRSF not only able to reduces the privacy leakage risk, satisfies legal and regulatory requirements but also allows various recommendation algorithms to be applied