Software teams are under continuous pressure to work effectively and achieve a high bar of performance. The data contained within software development lifecycle tools presents the opportunity to obtain visibility into DevOps metrics [12] , Flow metrics [17] , and other signals that provide insights into team effectiveness [14] . Such tool-based data can complement other information sources, such as employee surveys, towards a comprehensive picture of organization and team health [13] . Moreover, managing work across multiple teams requires a high level of visibility into the work of those teams, to inform decisions on team velocity, resource allocation, and return on investment. Since much of the work is conducted in software development tools, they are an essential source for consolidating and presenting a clear picture of that work. As organizations strive to rip the benefits that location flexibility offers for employee outcomes [3] and shift to hybrid or remote work, the reliance on software development tools to obtain that level of visibility is likely to increase.

Agile software development prioritizes the delivery of working software. However, there are challenges in the sprint planning process that could impact the reliability of sprint delivery. In this paper, we list three challenges related to the sprint planning process. We also discuss future work directions to facilitate the sprint planning process and mitigate those challenges for software teams.

Online social networks offer convenient ways to reach out to large audiences. In particular, Facebook pages are increasingly used by businesses, brands, and organizations to connect with multitudes of users worldwide. As the number of likes of a page has become a de-facto measure of its popularity and profitability, an underground market of services artificially inflating page likes (“like farms ”) has emerged alongside Facebook’s official targeted advertising platform. Nonetheless, besides a few media reports, there is little work that systematically analyzes Facebook pages’ promotion methods. Aiming to fill this gap, we present a honeypot-based comparative measurement study of page likes garnered via Facebook advertising and from popular like farms. First, we analyze likes based on demographic, temporal, and social characteristics and find 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. Next, we look at fraud detection algorithms currently deployed by Facebook and show that they do not work well to detect stealthy farms that spread likes over longer timespans and like popular pages to mimic regular users. To overcome their limitations, we investigate the feasibility of timeline-based detection of like farm accounts, focusing on characterizing content generated by Facebook accounts on their timelines as an indicator of genuine versus fake social activity. We analyze a wide range of features extracted from timeline posts, which we group into two main categories: lexical and non-lexical. We find that like farm accounts tend to re-share content more often, use fewer words and poorer vocabulary, and more often generate duplicate comments and likes compared to normal users. Using relevant lexical and non-lexical features, we build a classifier to detect like farms accounts that achieves a precision higher than 99% and a 93% recall.

The minimization of the logistic loss is a popular approach to batch supervised learning. Our paper starts from the surprising observation that, when fitting linear classifiers, the minimization of the logistic loss is \textitequivalent to the minimization of an exponential \textitrado-loss computed (i) over transformed data that we call Rademacher observations (rados), and (ii) over the \textitsame classifier as the one of the logistic loss. Thus, a classifier learnt from rados can be \textitdirectly used to classify \textitobservations. We provide a learning algorithm over rados with boosting-compliant convergence rates on the \textitlogistic loss (computed over examples). Experiments on domains with up to millions of examples, backed up by theoretical arguments, display that learning over a small set of random rados can challenge the state of the art that learns over the \textitcomplete set of examples. We show that rados comply with various privacy requirements that make them good candidates for machine learning in a privacy framework. We give several algebraic, geometric and computational hardness results on reconstructing examples from rados. We also show how it is possible to craft, and efficiently learn from, rados in a differential privacy framework. Tests reveal that learning from differentially private rados brings non-trivial privacy vs accuracy tradeoffs.

Recommender systems are increasingly becoming an integral part of on-line services. As the recommendations rely on personal user information, there is an inherent loss of privacy resulting from the use of such systems. While several works studied privacy-enhanced neighborhood-based recommendations, little attention has been paid to privacy preserving latent factor models, like those represented by matrix factorization techniques. In this paper, we address the problem of privacy preserving matrix factorization by utilizing differential privacy, a rigorous and provable privacy preserving method. We propose and study several approaches for applying differential privacy to matrix factorization, and evaluate the privacy-accuracy trade-offs offered by each approach. We show that input perturbation yields the best recommendation accuracy, while guaranteeing a solid level of privacy protection.

Internet censorship is enforced by numerous governments worldwide, however, due to the lack of publicly available information, as well as the inherent risks of performing active measurements, it is often hard for the research community to investigate censorship practices in the wild. Thus, the leak of 600GB worth of logs from 7 Blue Coat SG-9000 proxies, deployed in Syria to filter Internet traffic at a country scale, represents a unique opportunity to provide a detailed snapshot of a real-world censorship ecosystem.

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.

Recommender systems are widely used by online retailers to promote products and content that are most likely to be of interest to a specific customer. In such systems, users often implicitly or explicitly rate products they have consumed, and some form of collaborative filtering is used to find other users with similar tastes to whom the products can be recommended. While users can benefit from more targeted and relevant recommendations, they are also exposed to greater risks of privacy loss, which can lead to undesirable financial and social consequences. The use of obfuscation techniques to preserve the privacy of user ratings is well studied in the literature. However, works on obfuscation typically assume that all users uniformly apply the same level of obfuscation. In a heterogeneous environment, in which users adopt different levels of obfuscation based on their comfort level, the different levels of obfuscation may impact the users in the system in a different way. In this work we consider such a situation and make the following contributions: (a) using an offline dataset, we evaluate the privacy-utility trade-off in a system where a varying portion of users adopt the privacy preserving technique. Our study highlights the effects that each user's choices have, not only on their own experience but also on the utility that other users will gain from the system, and (b) we propose Privacy Canary, an interactive system that enables users to directly control the privacy-utility trade-off of the recommender system to achieve a desired accuracy while maximizing privacy protection, by probing the system via a private (i.e., undisclosed to the system) set of items. We evaluate the performance of our system with an off-line recommendations dataset, and show its effectiveness in balancing a target recommender accuracy with user privacy, compared to approaches that focus on a fixed privacy level.

Network outages are an important issue for Internet Service Providers (ISPs) and, more generally, online service providers, as they can result in major financial losses and negatively impact relationships with their customers. Troubleshooting network outages is a complex and time-consuming process. Network administrators are overwhelmed with large volumes of monitoring data and are limited to using very basic tools for debugging, e.g., ping and traceroute. Intelligent correlation of measurements from different Internet locations is very useful for analyzing the root cause of outages. However, correlating measurements of user traffic across domains is largely avoided as it raises privacy concerns. A possible solution is secure multi-party computation (MPC), a set of cryptographic methods that enable a number of parties to aggregate data in a privacy-preserving manner. In this work, we describe a novel system that helps diagnose network outages by correlating passive measurements from multiple ISPs in a privacy-preserving manner. We first show how MPC can be used to compute the scope (local, global, or semi-global) and severity (number of affected hosts) of network outages. To meet near-real-time monitoring guarantees, we then present an efficient protocol for MPC multiset union that uses counting Bloom filters (CBF) to drastically accelerate MPC comparison operations. Finally, we demonstrate the utility of our scheme using real-world traffic measurements from a national ISP and we discuss the trade-offs of the CBF-based computation.

In this paper, we investigate how the concept of differential privacy can be applied to Intelligent Transportation Systems (ITS), focusing on protection of Floating Car Data (FCD) stored and processed in central Traffic Data Centers (TDC). We illustrate an integration of differential privacy with privacy policy languages and policy-enforcement frameworks like the PRECIOSA PeRA architecture. Next, we identify differential privacy mechanisms to be integrated within the policy-enforcement framework and provide guidelines for the calibration of parameters to ensure specific privacy guarantees, while still supporting the level of accuracy required for ITS applications. We also discuss the challenges that the support of user-level differential privacy presents and outline a potential solution. As a result, we show that differential privacy could be put to practical use in ITS to enable strong protection of users' personal data.

We consider the problem of data mining with formal privacy guarantees, given a data access interface based on the differential privacy framework. Differential privacy requires that computations be insensitive to changes in any particular individual's record, thereby restricting data leaks through the results. The privacy preserving interface ensures unconditionally safe access to the data and does not require from the data miner any expertise in privacy. However, as we show in the paper, a naive utilization of the interface to construct privacy preserving data mining algorithms could lead to inferior data mining results. We address this problem by considering the privacy and the algorithmic requirements simultaneously, focusing on decision tree induction as a sample application. The privacy mechanism has a profound effect on the performance of the methods chosen by the data miner. We demonstrate that this choice could make the difference between an accurate classifier and a completely useless one. Moreover, an improved algorithm can achieve the same level of accuracy and privacy as the naive implementation but with an order of magnitude fewer learning samples.

In this paper we explore an approach to privacy preserving data mining that relies on the k -anonymity model. The k -anonymity model guarantees that no private information in a table can be linked to a group of less than k individuals. We suggest extended definitions of k -anonymity that allow the k -anonymity of a data mining model to be determined. Using these definitions, we present decision tree induction algorithms that are guaranteed to maintain k -anonymity of the learning examples. Experiments show that embedding anonymization within the decision tree induction process provides better accuracy than anonymizing the data first and inducing the tree later.