Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

So far in this course we have dealt entirely with the evolution of characters that are controlled by simple Mendelian inheritance at a single locus. There are notes on the course website about gametic disequilibrium and how allele frequencies change at two loci simultaneously, but we didn’t discuss them. In every example we’ve considered we’ve imagined that we could understand something about evolution by examining the evolution of a single gene. That’s the domain of classical population genetics. For the next few weeks we’re going to be exploring a field that’s actually older than classical population genetics, although the approach we’ll be taking to it involves the use of population genetic machinery. If you know a little about the history of evolutionary biology, you may know that after the rediscovery of Mendel’s work in 1900 there was a heated debate between the “biometricians” (e.g., Galton and Pearson) and the “Mendelians” (e.g., de Vries, Correns, Bateson, and Morgan). Biometricians asserted that the really important variation in evolution didn’t follow Mendelian rules. Height, weight, skin color, and similar traits seemed to

Human biochemical genetics

Today’s artificial intelligence still faces two major challenges. One is that, in most industries, data exists in the form of isolated islands. The other is the strengthening of data privacy and security. We propose a possible solution to these challenges: secure federated learning. Beyond the federated-learning framework first proposed by Google in 2016, we introduce a comprehensive secure federated-learning framework, which includes horizontal federated learning, vertical federated learning, and federated transfer learning. We provide definitions, architectures, and applications for the federated-learning framework, and provide a comprehensive survey of existing works on this subject. In addition, we propose building data networks among organizations based on federated mechanisms as an effective solution to allowing knowledge to be shared without compromising user privacy.

Federated Machine Learning: Concept and Applications

Mash extends the MinHash dimensionality-reduction technique to include a pairwise mutation distance and P value significance test, enabling the efficient clustering and search of massive sequence collections. Mash reduces large sequences and sequence sets to small, representative sketches, from which global mutation distances can be rapidly estimated. We demonstrate several use cases, including the clustering of all 54,118 NCBI RefSeq genomes in 33 CPU h; real-time database search using assembled or unassembled Illumina, Pacific Biosciences, and Oxford Nanopore data; and the scalable clustering of hundreds of metagenomic samples by composition. Mash is freely released under a BSD license (
 https://github.com/marbl/mash
 ).

/pdf/mash-fast-genome-and-metagenome-distance-estimation-using-3b84vfnedh.pdf

Mash: fast genome and metagenome distance estimation using MinHash.

Today's AI still faces two major challenges. One is that in most industries, data exists in the form of isolated islands. The other is the strengthening of data privacy and security. We propose a possible solution to these challenges: secure federated learning. Beyond the federated learning framework first proposed by Google in 2016, we introduce a comprehensive secure federated learning framework, which includes horizontal federated learning, vertical federated learning and federated transfer learning. We provide definitions, architectures and applications for the federated learning framework, and provide a comprehensive survey of existing works on this subject. In addition, we propose building data networks among organizations based on federated mechanisms as an effective solution to allow knowledge to be shared without compromising user privacy.

One embodiment of the present invention provides a system for privacy-sensitive ranking of aggregated data. During operation, the system distributes secret keys to a plurality of devices. The system then generates a plurality of probability density functions in a privacy-preserving way using encrypted data received from a subset of the plurality of devices. The encrypted data is data that has been encrypted with one or more of the secret keys by the subset of devices. The system then generates a plurality of probability mass functions, each probability mass function associated with a corresponding probability density function. Subsequently, the system computes a plurality of distance values, each respective distance value being a measure of distance from a probability mass function to a second distribution. The system then ranks the probability mass functions and/or associated attributes according to their respective distance from the second distribution.

Privacy-sensitive ranking of user data

The present invention provides a means and method of exhausting air from, and sealing a container of the type used for sealing food products. The means includes a self adhering patch for attachment to the exterior side wall of the container, or alternately, a patch that is constructed as part of a commercial container such as a baggie, and a suction probe which may be attached to a source of suction such as a suction pump for drawing air from the container when the suction probe is inserted through the patch and into the container. When the air has been removed, the probe is removed and the patch self-closes to seal the puncture.

Harvesting SSL Certificate Data to Mitigate Web-Fraud

Recent progress in genomics is bringing genetic testing to the masses, as participatory public initiatives are well underway to sequence the genome of millions of volunteers, and a new market is booming with a number of companies like 23andMe and AncestryDNA offering affordable tests directly to consumers Consequently, news, experiences, and views on genetic testing are increasingly shared and discussed online and on social networks like Twitter 
In this paper, we present a large-scale analysis of Twitter discourse on genetic testing We collect 302K tweets from 113K users, posted over 25 years, by using thirteen keywords related to genetic testing companies and public initiatives as search keywords We study both the tweets and the users posting them along several axes, aiming to understand who tweets about genetic testing, what they talk about, and how they use Twitter for that Among other things, we find that tweets about genetic testing originate from accounts that overall appear to be interested in digital health and technology Also, marketing efforts as well as announcements, such as the FDA's suspension of 23andMe's health reports, influence the type and the nature of user engagement Finally, we report on users who share screenshots of their results, and raise a few ethical and societal questions as we find evidence of groups associating genetic testing to racist ideologies

"23andMe confirms: I'm super white" - Analyzing Twitter Discourse On Genetic Testing.

Aggregate location data is often used to support smart services and applications, e.g., generating live traffic maps or predicting visits to businesses. In this paper, we present the first study on the feasibility of membership inference attacks on aggregate location time-series. We introduce a game-based definition of the adversarial task, and cast it as a classification problem where machine learning can be used to distinguish whether or not a target user is part of the aggregates. 
We empirically evaluate the power of these attacks on both raw and differentially private aggregates using two mobility datasets. We find that membership inference is a serious privacy threat, and show how its effectiveness depends on the adversary's prior knowledge, the characteristics of the underlying location data, as well as the number of users and the timeframe on which aggregation is performed. Although differentially private mechanisms can indeed reduce the extent of the attacks, they also yield a significant loss in utility. Moreover, a strategic adversary mimicking the behavior of the defense mechanism can greatly limit the protection they provide. Overall, our work presents a novel methodology geared to evaluate membership inference on aggregate location data in real-world settings and can be used by providers to assess the quality of privacy protection before data release or by regulators to detect violations.

Knock Knock, Who's There? Membership Inference on Aggregate Location Data

While location data is extremely valuable for various applications, disclosing it prompts serious threats to individuals' privacy. To limit such concerns, organizations often provide analysts with aggregate time-series that indicate, e.g., how many people are in a location at a time interval, rather than raw individual traces. In this paper, we perform a measurement study to understand Membership Inference Attacks (MIAs) on aggregate location time-series, where an adversary tries to infer whether a specific user contributed to the aggregates. 
We find that the volume of contributed data, as well as the regularity and particularity of users' mobility patterns, play a crucial role in the attack's success. We experiment with a wide range of defenses based on generalization, hiding, and perturbation, and evaluate their ability to thwart the attack vis-a-vis the utility loss they introduce for various mobility analytics tasks. 
Our results show that some defenses fail across the board, while others work for specific tasks on aggregate location time-series. For instance, suppressing small counts can be used for ranking hotspots, data generalization for forecasting traffic, hotspot discovery, and map inference, while sampling is effective for location labeling and anomaly detection when the dataset is sparse. Differentially private techniques provide reasonable accuracy only in very specific settings, e.g., discovering hotspots and forecasting their traffic, and more so when using weaker privacy notions like crowd-blending privacy. Overall, our measurements show that there does not exist a unique generic defense that can preserve the utility of the analytics for arbitrary applications, and provide useful insights regarding the disclosure of sanitized aggregate location time-series.

Emiliano De Cristofaro

Papers

Privacy-sensitive ranking of user data

Harvesting SSL Certificate Data to Mitigate Web-Fraud

"23andMe confirms: I'm super white" - Analyzing Twitter Discourse On Genetic Testing.

Knock Knock, Who's There? Membership Inference on Aggregate Location Data

Measuring Membership Privacy on Aggregate Location Time-Series.