Privacy-preserving friend recommendations in online social networks

Online social networks, such as Facebook and Google+, have been emerging as a new communication service for users to stay in touch and share information with family members and friends over the Internet. Since the users are generating huge amounts of data on social network sites, an interesting question is how to mine this enormous amount of data to retrieve useful information. Along this direction, social network analysis has emerged as an important tool for many business intelligence applications such as identifying potential customers and promoting items based on their interests. In particular, since users are often interested to make new friends, a friend recommendation application provides the medium for users to expand his/her social connections and share information of interest with more friends. Besides this, it also helps to enhance the development of the entire network structure. The existing friend recommendation methods utilize social network structure and/or user profile information. However, these methods can no longer be applicable if the privacy of users is taken into consideration. This work introduces a set of privacy-preserving friend recommendation protocols based on different existing similarity metrics in the literature. Briefly, depending on the underlying similarity metric used, the proposed protocols guarantee the privacy of a user\u27s personal information such as friend lists. These protocols are the first to make the friend recommendation process possible in privacy-enhanced social networking environments. Also, this work considers the case of outsourced social networks, where users\u27 profile data are encrypted and outsourced to third-party cloud providers who provide social networking services to the users. Under such an environment, this work proposes novel protocols for the cloud to do friend recommendations in a privacy-preserving manner --Abstract, page iii

Privacy-Preserving Outsourced Collaborative Frequent Itemset Mining in the Cloud

Big Data management and analytics has revolutionized the way how organizations collect, store, process and retrieve, huge volumes of data. In order to fully leverage the potential of big data, it is often that organizations need to collaborate and analyze their combined data, and thus, improving the accuracy of results. However, due to government regulations and internal privacy policies, organizations cannot freely share their data with one another. Existing secure multiparty computation techniques along this direction are very expensive. In this paper, we develop a protocol that facilitates multiple users to outsource their encrypted databases as well as the frequent itemset mining task to a cloud environment in a collaborative and privacy-preserving manner. Our solution is built using the well-known apriori algorithm in order to boost the performance of frequent itemset mining in the cloud. Our comprehensive analysis has demonstrated that the proposed solution preserves the confidentiality of participating users. Additionally, our solution ensures that the entire frequent itemset mining task is performed on the cloud-side, thereby fully utilizing the cloud computing services to handle big data needs and incurring negligible cost on the end-users

Secure Multiset Intersection Cardinality and Its Application to Jaccard Coefficient

The Jaccard Coefficient, as an information similarity measure, has wide variety of applications, such as cluster analysis and image segmentation. Due to the concerns of personal privacy, the Jaccard Coefficient cannot be computed directly between two independently owned datasets. The problem, secure computation of the Jaccard Coefficient for multisets (SJCM), considers the situation where two parties want to securely compute the random shares of the Jaccard Coefficient between their multisets. During the process, the content of each party\u27s multiset is not disclosed to the other party and also the value of Jaccard Coefficient should be hidden from both parties. Secure computation of multiset intersection cardinality is an important sub-problem of SJCM. Existing methods when applied to solve such a problem can lead to either insecure or inefficient solutions. Our work addresses this gap. We first present a basic SJCM protocol constructed using the existing secure dot product method as a sub-routine. Then, as a major contribution, we propose an approximated version of our basic protocol to improve efficiency without compromising accuracy much. We provide various experimental results to show that the proposed protocols are significantly more efficient than the existing techniques when the domain size is small using both simulated and real datasets

A Secure and Distributed Framework to Identify and Share Needed Information

Information analysis and communication play significant roles in decision making, especially in battle grounds and situations where national security is under threat. In many situations when information in consideration is sensitive/confidential, it is in our best interests to analyze and share only needed information to minimize the potential of security breach regarding other irrelevant but sensitive information. Thus, the goal of this paper is to investigate the required methodologies and propose an advanced communication framework that enables different entities in distributed environments to identify, share and analyze only needed information, without disclosing other unwanted but sensitive information. Such a framework will enable the sharing of crucial protected information, which would not have been possible before. For example, to access protected or confidential intelligence information, a person needs to have sufficient clearance level and to justify the need-to-know basis. If the person cannot disclose what he or she knows and without him or her knowing the protected information in the first place, objectively justifying the need-to-know is very difficult. The proposed framework will provide an objective and secure way to justify the need-to-know basis and to identify and share the needed intelligence without disclosing any irrelevant but protected information. In addition, it can also minimize information disclosure in coordinated and collaborative intelligence gathering involving multiple entities

Secure Similar Document Detection: Optimized Computation using the Jaccard Coefficient

Secure Similar Document Detection (SSDD) problem considers two parties, each holding a private document, who want to compute the similarity between their documents without leaking the document contents to one another. This is a unique problem whose applications span across a variety of domains, including the medical field, military intelligence, and academia. In this paper, we aim to solve the SSDD problem by representing documents as multisets and using the Jaccard Coefficient (JC) as a similarity measure. We first illustrate how the computation of Jaccard Coefficient can be reduced to the computation of intersection size between the multisets. Then, we propose a novel way to securely approximate the size of intersection between multisets using Bloom filters and hash functions, without significant reductions in security and accuracy. Our proposed protocol exploits a unique property of Bloom filters -that computing the dot product of two Bloom filters yields their intersection size

Structural and Message Based Private Friend Recommendation

The emerging growth of online social networks have opened new doors for various business applications such as promoting a new product across its customers. Besides this, friend recommendation is an important tool for recommending potential candidates as friends to users in order to enhance the development of the entire network structure. Existing friend recommendation methods utilize social network structure and/or user profile information. However, these techniques can no longer be applicable if the privacy of users is taken into consideration. In this paper, we propose a two-phase private friend recommendation protocol for recommending friends to a given target user based on the network structure as well as utilizing the real message interaction between users. Our protocol computes the recommendation scores of all users who are within a radius of h from the target user in a privacy preserving manner. In addition, we show the practical applicability of our approach through empirical analysis

Efficient Privacy-Preserving Range Queries Over Encrypted Data in Cloud Computing

With the growing popularity of data and service outsourcing, where the data resides on remote servers in encrypted form, there remain open questions about what kind of query operations can be performed on the encrypted data. In this paper, we focus on one such important query operation, namely range query. One of the basic security primitive that can be used to evaluate range queries is secure comparison of encrypted integers. However, the existing secure comparison protocols strongly rely on the encrypted bit-wise representations rather than on pure encrypted integers. Therefore, in this paper, we first propose an efficient method for converting an encrypted integer z into encryptions of the individual bits of z. We then utilize the proposed security primitive to construct a new protocol for secure evaluation of range queries in the cloud computing environment. Furthermore, we empirically show the efficiency gains of using our security primitive over existing method under the range query application

Interest-Driven Private Friend Recommendation

The emerging growth of online social networks has opened new doors for various kinds of applications such as business intelligence and expanding social connections through friend recommendations. In particular, friend recommendation facilitates users to explore new friendships based on social network structures, user profile information (similar interest) or both. However, as the privacy concerns of users are on the rise, searching for new friends is not a straightforward task under the assumption that users’ information is kept private. Along this direction, this paper proposes two private friend recommendation algorithms based on the social network structure and the users’ social tags. The first protocol is more efficient from a user’s perspective compared to the second protocol, and this efficiency gain comes at the expense of relaxing the underlying privacy assumptions. On the other hand, the second protocol provides the best security guarantee. In addition, we empirically analyze the complexities of the proposed protocols and provide various experimental results

Security with Privacy-a Research Agenda

Data is one of the most valuable assets for organization. It can facilitate users or organizations to meet their diverse goals, ranging from scientific advances to business intelligence. Due to the tremendous growth of data, the notion of big data has certainly gained momentum in recent years. Cloud computing is a key technology for storing, managing and analyzing big data. However, such large, complex, and growing data, typically collected from various data sources, such as sensors and social media, can often contain personally identifiable information (PII) and thus the organizations collecting the big data may want to protect their outsourced data from the cloud. In this paper, we survey our research towards development of efficient and effective privacy-enhancing (PE) techniques for management and analysis of big data in cloud computing.We propose our initial approaches to address two important PE applications: (i) privacy-preserving data management and (ii) privacy-preserving data analysis under the cloud environment. Additionally, we point out research issues that still need to be addressed to develop comprehensive solutions to the problem of effective and efficient privacy-preserving use of data

Optimized Secure Data Aggregation in Wireless Sensor Networks

With continuing developments in miniaturization and battery design, wireless sensor networks (WSNs) are poised to become common technology in our daily lives. Low cost and flexibility of deployment make WSNs well suited for a wide variety of military, environmental, healthcare, and commercial applications. Some WSN applications, such as monitoring patients in hospitals or weapons targeting in battlefront require endto- end data confidentiality. However, since WSNs are made up of many resource limited sensor nodes, they are typically unable to sustain the high volumes of data transmissions. Using innetwork data aggregation, sensor data from multiple nodes can be combined before being forwarded to neighboring nodes; and thus, energy consumption can be reduced significantly. But in situations where sensor nodes privacy is non-negotiable, data aggregation cannot be implemented at the cost of security. Therefore, there is a strong need for secure data aggregation (SDA) protocols designed to fit the unique properties and considerable constraints of WSNs. Existing end-to-end solutions are either insecure or impractical. In this paper, we propose a novel solution for the secure aggregation of data in WSNs based on probabilistic homomorphic encryption. By combining with a unique encoding function, our solution guarantees the privacy of sensor data, while also greatly reducing communication costs