In this paper we consider the problem where a client wishes to subscribe to some product or service provided by a server, but maintain their anonymity. At the same time, the server must be able to authenticate the client as a genuine user and be able to discontinue (or revoke) the client's access if the subscription fees are not paid. Current solutions for this problem are typically constructed using some combination of blind signature or zero-knowledge proof techniques, which do not directly support client revocation (that is, revoking a user before expiry of their secret value). In this paper, we present a solution for this problem on the basis of the broadcast encryption scheme, suggested by Boneh et al., by which the server can broadcast a secret to a group of legitimate clients. Our solution allows the registered client to log into the server anonymously and also supports client revocation by the server. Our solution can be used in many applications, such as location-based queries. We formally define a model for our anonymous subscription protocol and prove the security of our solution under this model. In addition, we present experimental results from an implementation of our protocol. These experimental results demonstrate that our protocol is practical.

In this paper, we consider a scenario where a user queries a user profile database, maintained by a social networking service provider, to identify users whose profiles match the profile specified by the querying user. A typical example of this application is online dating. Most recently, an online dating website, Ashley Madison, was hacked, which resulted in a disclosure of a large number of dating user profiles. This data breach has urged researchers to explore practical privacy protection for user profiles in a social network. In this paper, we propose a privacy-preserving solution for profile matching in social networks by using multiple servers. Our solution is built on homomorphic encryption and allows a user to find out matching users with the help of multiple servers without revealing to anyone the query and the queried user profiles in clear. Our solution achieves user profile privacy and user query privacy as long as at least one of the multiple servers is honest. Our experiments demonstrate that our solution is practical.

In an edge-enabled data management and computing environment, it is critical to ensure the privacy of the information acquired, processed, and exchanged among the different parties. The problem is complex because of the large scale, mobility, device, and protocol heterogeneity. Also, unlike in conventional environments, communication may be fragmented and portions of the environment can be physically unprotected. To date, there are several privacy-enhancing techniques, such as secure multiparty computation techniques, private information retrieval (PIR), and data sanitization techniques. However, there is not a single technique that works for all possible uses of the data in edge systems. In addition, these techniques are computationally expensive and thus may not be suitable for edge devices. In this paper, we first cover basic privacy building blocks, including differential privacy and homomorphic encryption. We then discuss privacy solutions specific to three different types of data use that are relevant for edge-based applications: data aggregation techniques, point-of-interest (POI) services and traffic information services, and crowdsourcing. These applications have been selected as they provide a broad spectrum of edge computing applications. Throughout this paper, we outline open research directions.

Recently, based on the Paillier cryptosystem [1] , Yi et al. outline a distributed ElGamal cryptosystem which allows for both a much simpler distributed key generation procedure and a more efficient distributed decryption of messages from a large plaintext domain [2] . In this paper, we analyze the security of their proposed variant of ElGamal encryption scheme and demonstrate that their proposed variant is not secure as claimed. Thus, whether an additively homomorphic cryptosytem satisfying the desired properties exists remains an open question.

Private record linkage protocols allow multiple parties to exchange matching records, which refer to the same entities or have similar values, while keeping the non-matching ones secret. Conventional protocols are based on computationally expensive cryptographic primitives and therefore do not scale. To address these scalability issues, hybrid protocols have been proposed that combine differential privacy techniques with secure multiparty computation techniques. However, a drawback of such protocols is that they disclose to the parties both the matching records and the differentially private synopses of the datasets involved in the linkage. Consequently, differential privacy is no longer always satisfied. To address this issue, we propose a novel framework that separates the private synopses from the matching records. The two parties do not access the synopses directly, but still use them to efficiently link records. We theoretically prove the security of our framework under the state-of-the-art privacy notion of differential privacy for record linkage (DPRL). In addition, we develop a simple but effective strategy for releasing private synopses. Extensive experimental results show that our framework is superior to the existing methods in terms of efficiency.

In Spatial Crowdsourcing (SC), a set of spatio-temporal tasks are outsourced to a set of workers, i.e., individuals with mobile devices who physically travel to task locations. The process of matching workers to tasks is performed by a SC server. To perform matching, the SC server needs access to worker locations. However, the SC server may not be trustworthy. Current solutions for protecting locations of workers assume that a trusted cellular service provider (CSP) knows the identities and locations of workers and sanitizes locations before sharing them with the SC server. In practice, the CSP may not have the technical ability, nor the proper incentives to perform the sanitization task. Thus, location protection must be performed by a Location Privacy Provider (LPP). To prevent identity disclosure to the LPP, we propose a novel solution based on anonymous credentials which preserves worker privacy. Our solution allows registered workers to log on to the LPP and receive tasks from the SC-server anonymously. In addition, our solution assures the confidentiality and integrity of spatial tasks. Our implementation and experiments demonstrate that our solution is practical.

In this paper, we consider a scenario where a user queries a user profile database, maintained by a social networking service provider, to find out some users whose profiles are similar to the profile specified by the querying user. A typical example of this application is online dating. Most recently, an online data site, Ashley Madison, was hacked, which results in disclosure of a large number of dating user profiles. This serious data breach has urged researchers to explore practical privacy protection for user profiles in online dating. In this paper, we give a privacy-preserving solution for user profile matching in social networks by using multiple servers. Our solution is built on homomorphic encryption and allows a user to find out some matching users with the help of the multiple servers without revealing to anyone privacy of the query and the queried user profiles. Our solution achieves user profile privacy and user query privacy as long as at least one of the multiple servers is honest. Our implementation and experiments demonstrate that our solution is practical.

In a two-server password-authenticated key exchange (PAKE) protocol, a client splits its password and stores two shares of its password in the two servers, respectively, and the two servers then cooperate to authenticate the client without knowing the password of the client. In case one server is compromised by an adversary, the password of the client is required to remain secure. In this paper, we present two compilers that transform any two-party PAKE protocol to a two-server PAKE protocol on the basis of the identity-based cryptography, called ID2S PAKE protocol. By the compilers, we can construct ID2S PAKE protocols which achieve implicit authentication. As long as the underlying two-party PAKE protocol and identity-based encryption or signature scheme have provable security without random oracles, the ID2S PAKE protocols constructed by the compilers can be proven to be secure without random oracles. Compared with the Katz et al.'s two-server PAKE protocol with provable security without random oracles, our ID2S PAKE protocol can save from 22 to 66 percent of computation in each server.

Recently, the paradigm of data mining-as-a-service in cloud computing environment has been attracting interests. In this paradigm, a company (data owner), lacking data storage, computational resources and expertise, stores its data in the cloud and outsources its mining tasks to the cloud service provider (server). In order to protect the privacy of the outsourced database and the association rules mined, k-anonymity, k-support, and k-privacy techniques have been proposed to perturb the data before it is uploaded to the server. These techniques are computationally expensive. If the data owner has resources to use these techniques, then it is often able to execute association rule mining locally. In this paper, we consider a scenario where a user (data owner) encrypts its data and stores it in the cloud. To mine association rules from its data, the user outsources the task to n (≥ 2) "semi-honest" servers, which cooperate to perform association rule mining on the encrypted data in the cloud and return encrypted association rules to the user. In this setting, we provide three solutions to protecting data privacy during association rule mining. Our solutions are built on the distributed ElGamal cryptosystem and achieve item privacy, transaction privacy and database privacy, respectively, as long as at least one out of the n servers is honest. To reduce the possibility that all servers are compromised, the user can use servers from different cloud providers. Our implementation and experiments demonstrate that our solutions are practical.

Private record linkage protocols allow multiple parties to exchange matching records, which refer to the same entities or have similar values, while keeping the non-matching ones secret. Conventional protocols are based on computationally expensive cryptographic primitives and therefore do not scale. To address these scalability issues, hybrid protocols have been recently proposed that combine differential privacy techniques with secure multiparty computation techniques. However, a drawback of such protocols is that they disclose to the parties both the matching records and the differentially private synopses of the datasets involved in the linkage. Consequently, differential privacy is no longer always satisfied. To address this issue, we propose a novel framework, which separates the private synopses from the matching records. The two parties do not access the synopses directly, but still use them to efficiently link records. We theoretically prove the security of our framework. In addition, we have developed a simple but effective strategy for releasing private synopses. Extensive experimental results show that our framework is superior to the existing methods in terms of both recall rate and efficiency.

Extracting useful knowledge from social network datasets is a challenging problem. While large online social networks such as Facebook and LinkedIn are well known and gather millions of users, small social networks are today becoming increasingly common. Many corporations already use existing social networks to connect to their customers. Seeing the increasing usage of small social networks, such companies will likely start to create in-house online social networks where they will own the data shared by customers. The trustworthiness of these online social networks is essentially important for decision making of those companies. In this paper, our goal is to assess the trustworthiness of local social network data by referencing external social networks. To add to the difficulty of this problem, privacy concerns that exist for many social network datasets have restricted the ability to analyze these networks and consequently to maximize the knowledge that can be extracted from them. This paper addresses this issue by introducing the problem of data trustworthiness in social networks when repositories of anonymized social networks exist that can be used to assess such trustworthiness. Three trust score computation models (absolute, relative, and weighted) that can be instantiated for specific anonymization models are defined and algorithms to calculate these trust scores are developed. Using both real and synthetic social networks, the usefulness of the trust score computation is validated through a series of experiments.

Extracting useful knowledge from social network datasets is a challenging problem. To add to the difficulty of this problem, privacy concerns that exist for many social network datasets have restricted the ability to analyze these networks and consequently to maximize the knowledge that can be extra

Assessing the trustworthiness of location data corresponding to individuals is essential in several applications, such as forensic science and epidemic control. To obtain accurate and trustworthy location data, analysts must often gather and correlate information from several independent sources, e.g., physical observation, witness testimony, surveillance footage, etc. However, such information may be fraudulent, its accuracy may be low, and its volume may be insufficient to ensure highly trustworthy data. On the other hand, recent advancements in mobile computing and positioning systems, e.g., GPS-enabled cell phones, highway sensors, etc., bring new and effective technological means to track the location of an individual. Nevertheless, collection and sharing of such data must be done in ways that do not violate an individual's right to personal privacy.