Security and privacy aspects of mobile applications for post-surgical care

Mobile technologies have the potential to improve patient monitoring, medical decision making and in general the efficiency and quality of health delivery. They also pose new security and privacy challenges. The objectives of this work are to (i) Explore and define security and privacy requirements on the example of a post-surgical care application, and (ii) Develop and test a pilot implementation Post-Surgical Care Studies of surgical out- comes indicate that timely treatment of the most common complications in compliance with established post-surgical regiments greatly improve success rates. The goal of our pilot application is to enable physician to optimally synthesize and apply patient directed best medical practices to prevent post-operative complications in an individualized patient/procedure specific fashion. We propose a framework for a secure protocol to enable doctors to check most common complications for their patient during in-hospital post- surgical care. We also implemented our construction and cryptographic protocols as an iPhone application on the iOS using existing cryptographic services and libraries

Lower Bounds for Oblivious Near-Neighbor Search

We prove an $\Omega(d \lg n/ (\lg\lg n)^2)$ lower bound on the dynamic cell-probe complexity of statistically $\mathit{oblivious}$ approximate-near-neighbor search ($\mathsf{ANN}$) over the $d$-dimensional Hamming cube. For the natural setting of $d = \Theta(\log n)$, our result implies an $\tilde{\Omega}(\lg^2 n)$ lower bound, which is a quadratic improvement over the highest (non-oblivious) cell-probe lower bound for $\mathsf{ANN}$. This is the first super-logarithmic $\mathit{unconditional}$ lower bound for $\mathsf{ANN}$ against general (non black-box) data structures. We also show that any oblivious $\mathit{static}$ data structure for decomposable search problems (like $\mathsf{ANN}$) can be obliviously dynamized with $O(\log n)$ overhead in update and query time, strengthening a classic result of Bentley and Saxe (Algorithmica, 1980).Comment: 28 page

On Security of RASP Data Perturbation for Secure Half-Space Queries in the Cloud

Secure data intensive computing in the cloud is challenging, involving a complicated tradeoff among security, performance, extra costs, and cloud economics. Although fully homomorphic encryption is considered as the ultimate solution, it is still too expensive to be practical at the current stage. In contrast, methods that preserve special types of data utility, even with weaker security, might be acceptable in practice. The recently proposed RASP perturbation method falls into this category. It can provide practical solutions for specific problems such as secure range queries, statistical analysis, and machine learning. The RASP perturbation embeds the multidimensional data into a secret higher dimensional space, enhanced with random noise addition to protect the confidentiality of data. It also provides a query perturbation method to transform half-space queries to a quadratic form and, meanwhile, preserving the results of half-space queries. The utility preserving property and wide application domains are appealing. However, since the security of this method is not thoroughly analyzed, the risk of using this method is unknown. The purpose of this paper is to investigate the security of the RASP perturbation method based on a specific threat model. The threat model defines three levels of adversarial power and the concerned attacks. We show that although the RASP perturbed data and queries are secure on the lowest level of adversarial power, they do not satisfy the strong indistinguishability definition on higher levels of adversarial power. As we have noticed, the indistinguishability definition might not be too strong to be useful in the context of data intensive cloud computation. In addition, the noise component in the perturbation renders it impossible to exactly recover the plain data; thus, all attacks are essentially estimation attacks. We propose a weaker security definition based on information theoretic measures to describe the effectiveness of estimation attacks, and then study the security under this weaker definition. This security analysis helps clearly identify the security weaknesses of the RASP perturbation and quantify the expected security under different levels of adversarial power

Efficient Cloud Backup and Private Search

As organizations and companies are increasingly offloading data and computation to the cloud to reduce infrastructure administration,data volume keeps growing and new services and algorithms are needed to meet increasing demands for both storage capacity and privacy.The first part of my thesis will address cloud data backup.Organizations and companies often backup and archive high volumes of binary and text datasets for fault tolerance,internal investigation, and electronic discovery. Source-side deduplication has an advantage to avoid or minimize duplicated data transmitted over the network,however it demands more computing resource to perform extensive fingerprint comparison which would otherwise be available for primary services at the source.For data stored in the cloud, users need efficient, scalable services for searching these files.In the first part of this thesis, I will cover the key components of existing solutions for large-scale backup storage in the cloud.I will go into detail on how deduplication is important to large scale backup systems, and review some ongoing work.I will also detail my contributions in this area towards low-profile source-side deduplication.The second part of my thesis addresses an open problem for efficient private document search on data hosted on the cloud.As sensitive information is increasingly centralized into the cloud, for the protection of data privacy, such data is often encrypted,which makes effective data indexing and search a very challenging task. To overcome the challenges of querying encrypted datasets,searchable encryption schemes allow users to securely search over encrypted data through keywords.No existing solutions for efficient ranking which involves complex arithmetic computation in feature composition and scoring currently exist,and without relevant ranking of search results queries over very large datasets which may return many results can be impractical.In the second part of my thesis I will review existing work on private search and introduce our ongoing and published work for this open problem,focusing on how to make private search practical and scalable for large datasets

Prochlo: Strong Privacy for Analytics in the Crowd

The large-scale monitoring of computer users' software activities has become commonplace, e.g., for application telemetry, error reporting, or demographic profiling. This paper describes a principled systems architecture---Encode, Shuffle, Analyze (ESA)---for performing such monitoring with high utility while also protecting user privacy. The ESA design, and its Prochlo implementation, are informed by our practical experiences with an existing, large deployment of privacy-preserving software monitoring. (cont.; see the paper

Machine Learning Models that Remember Too Much

Machine learning (ML) is becoming a commodity. Numerous ML frameworks and services are available to data holders who are not ML experts but want to train predictive models on their data. It is important that ML models trained on sensitive inputs (e.g., personal images or documents) not leak too much information about the training data. We consider a malicious ML provider who supplies model-training code to the data holder, does not observe the training, but then obtains white- or black-box access to the resulting model. In this setting, we design and implement practical algorithms, some of them very similar to standard ML techniques such as regularization and data augmentation, that "memorize" information about the training dataset in the model yet the model is as accurate and predictive as a conventionally trained model. We then explain how the adversary can extract memorized information from the model. We evaluate our techniques on standard ML tasks for image classification (CIFAR10), face recognition (LFW and FaceScrub), and text analysis (20 Newsgroups and IMDB). In all cases, we show how our algorithms create models that have high predictive power yet allow accurate extraction of subsets of their training data