While existing social networking services tend to connect people who know each other, people show a desire to also connect to yet unknown people in physical proximity. Existing research shows that people tend to connect to similar people. Utilizing technology in order to stimulate human interaction between strangers, we consider the scenario of two strangers meeting. On the example of similarity in musical taste, we develop a solution for the problem of similarity estimation in proximity-based mobile social networks. We show that a single exchange of a probabilistic data structure between two devices can closely estimate the similarity of two users - without the need to contact a third-party server. We introduce metrics for fast and space-efficient approximation of the Dice coefficient of two multisets - based on the comparison of two Counting Bloom Filters or two Count-Min Sketches. Our analysis shows that utilizing a single hash function minimizes the error when comparing these probabilistic data structures. The size that should be chosen for the data structure depends on the expected average number of unique input elements. Using real user data, we show that a Counting Bloom Filter with a single hash function and a length of 128 is sufficient to accurately estimate the similarity between two multisets representing the musical tastes of two users. Our approach is generalizable for any other similarity estimation of frequencies represented as multisets.
Large scale data processing brings new challenges to the design of privacy-preserving protocols: how to meet the increasing requirements of speed and throughput of modern applications, and how to scale up smoothly when data being protected is big. Efficiency and scalability become critical criteria for privacy preserving protocols in the age of Big Data. In this paper, we present a new Private Set Intersection (PSI) protocol that is extremely efficient and highly scalable compared with existing protocols. The protocol is based on a novel approach that we call oblivious Bloom intersection. It has linear complexity and relies mostly on efficient symmetric key operations. It has high scalability due to the fact that most operations can be parallelized easily. The protocol has two versions: a basic protocol and an enhanced protocol, the security of the two variants is analyzed and proved in the semi-honest model and the malicious model respectively. A prototype of the basic protocol has been built. We report the result of performance evaluation and compare it against the two previously fastest PSI protocols. Our protocol is orders of magnitude faster than these two protocols. To compute the intersection of two million-element sets, our protocol needs only 41 seconds (80-bit security) and 339 seconds (256-bit security) on moderate hardware in parallel mode. Private set intersection enables two parties -- a client and a server -- to compute the intersection of their respective sets without disclosing anything else. It is a fundamental operation -- equivalent to a secure, distributed database join -- and has many applications particularly in privacy-preserving law enforcement. In this paper we present a novel protocol that has linear complexity, is secure in the malicious model without random oracles, is client set size-independent and efficient. Furthermore, the computation of the intersection can be outsourced to an oblivious service provider, as in secure cloud computing. We leverage a completely novel construction for computing the intersection using Bloom filter and homomorphic encryption. For outsourcing we require and introduce a new homomorphic encryption scheme which may be of independent interest. In this paper we propose a new approach to privately compute the set-union cardinality and the set-intersection cardinality among multiple honest-but-curious parties. Our approach is inspired by a proposal of Ashok and Mukkamala (DBSec’14) which deploys Bloom filters to approximate the size tightly. One advantage of their solution is that it avoids ample public-key cryptography. Unfortunately, we show here that their protocol is vulnerable to actual attacks. We therefore propose a new Bloom filter based protocol, also forgoing heavy cryptography, and prove its security.