Paying for Likes? Understanding Facebook like fraud using honeypots

Facebook pages offer an easy way to reach out to a very large audience as they can easily be promoted using Facebook's advertising platform. Recently, the number of likes of a Facebook page has become a measure of its popularity and profitability, and an underground market of services boosting page likes, aka like farms, has emerged. Some reports have suggested that like farms use a network of profiles that also like other pages to elude fraud protection algorithms, however, to the best of our knowledge, there has been no systematic analysis of Facebook pages' promotion methods. This paper presents a comparative measurement study of page likes garnered via Facebook ads and by a few like farms. We deploy a set of honeypot pages, promote them using both methods, and analyze garnered likes based on likers' demographic, temporal, and social characteristics. We highlight a few interesting findings, including that some farms seem to be operated by bots and do not really try to hide the nature of their operations, while others follow a stealthier approach, mimicking regular users' behavior

SplitBox: Toward Efficient Private Network Function Virtualization

This paper presents SplitBox, an efficient system for privacy-preserving processing of network functions that are outsourced as software processes to the cloud. Specifically, cloud providers processing the network functions do not learn the network policies instructing how the functions are to be processed. First, we propose an abstract model of a generic network function based on match-action pairs. We assume that this function is processed in a distributed manner by multiple honest-but-curious cloud service providers. Then, we introduce our SplitBox system for private network function virtualization and present a proof-of-concept implementation on FastClick, an extension of the Click modular router, using a firewall as a use case. Our experimental results achieve a throughput of over 2 Gbps with 1 kB-sized packets on average, traversing up to 60 firewall rules

Fast privacy-preserving network function outsourcing

In this paper, we present the design and implementation of SplitBox, a system for privacy-preserving processing of network functions outsourced to cloud middleboxes—i.e., without revealing the policies governing these functions. SplitBox is built to provide privacy for a generic network function that abstracts the functionality of a variety of network functions and associated policies, including firewalls, virtual LANs, network address translators (NATs), deep packet inspection, and load balancers. We present a scalable design aiming to provide high throughput and low latency, by distributing functionalities to a few virtual machines (VMs), while providing provably secure guarantees. We implement SplitBox inside FastClick, an extension of the Click modular router, using Intel's DPDK to handle packet I/O. We evaluate our prototype experimentally to find its bottlenecks and stress-test its different components, vis-à-vis two widely used network functions, i.e., firewall and VLAN tagging. Our evaluation shows that, on commodity hardware, SplitBox can process packets close to line rate (i.e., 8.9Gbps) with up to 50 traversed policies

The Cost of Privacy in Asynchronous Differentially-Private Machine Learning

We consider training machine learning models using data located on multiple private and geographically-scattered servers with different privacy settings. Due to the distributed nature of the data, communicating with all collaborating private data owners simultaneously may prove challenging or altogether impossible. We consider differentially-private asynchronous algorithms for collaboratively training machine-learning models on multiple private datasets. The asynchronous nature of the algorithms implies that a central learner interacts with the private data owners one-on-one whenever they are available for communication without needing to aggregate query responses to construct gradients of the entire fitness function. Therefore, the algorithm efficiently scales to many data owners. We define the cost of privacy as the difference between the fitness of a privacy-preserving machine-learning model and the fitness of trained machine-learning model in the absence of privacy concerns. We demonstrate that the cost of privacy has an upper bound that is inversely proportional to the combined size of the training datasets squared and the sum of the privacy budgets squared. We validate the theoretical results with experiments on financial and medical datasets. The experiments illustrate that collaboration among more than 10 data owners with at least 10,000 records with privacy budgets greater than or equal to 1 results in a superior machine-learning model in comparison to a model trained in isolation on only one of the datasets, illustrating the value of collaboration and the cost of the privacy. The number of the collaborating datasets can be lowered if the privacy budget is higher

The Value of Collaboration in Convex Machine Learning with Differential Privacy

In this paper, we apply machine learning to distributed private data owned by multiple data owners, entities with access to non-overlapping training datasets. We use noisy, differentially-private gradients to minimize the fitness cost of the machine learning model using stochastic gradient descent. We quantify the quality of the trained model, using the fitness cost, as a function of privacy budget and size of the distributed datasets to capture the trade-off between privacy and utility in machine learning. This way, we can predict the outcome of collaboration among privacy-aware data owners prior to executing potentially computationally-expensive machine learning algorithms. Particularly, we show that the difference between the fitness of the trained machine learning model using differentially-private gradient queries and the fitness of the trained machine model in the absence of any privacy concerns is inversely proportional to the size of the training datasets squared and the privacy budget squared. We successfully validate the performance prediction with the actual performance of the proposed privacy-aware learning algorithms, applied to: financial datasets for determining interest rates of loans using regression; and detecting credit card frauds using support vector machines