SECURE IMAGE PROCESSING

In todays heterogeneous network environment, there is a growing demand for distrusted parties to jointly execute distributed algorithms on private data whose secrecy needed to be safeguarded. Platforms that support such computation on image processing purposes are called secure image processing protocols. In this thesis, we propose a new security model, called quasi information theoretic (QIT) security. Under the proposed model efficient protocols on two basic image processing algorithms linear filtering and thresholding are developed. For both problems we consider two situations: 1) only two parties are involved where one holds the data and the other possesses the processing algorithm; 2) an additional non-colluding third party exists. Experiments show that our proposed protocols improved the computational time significantly compared with the classical cryptographical couterparts as well as providing reasonable amount of security as proved in the thesi

Generating Private Recommendation System Using Multiple Homomorphic Encryption Scheme

The recommender system is important tool in online application to generate the recommendation services. Recommendations are generated by collecting the data from users need; online services access the user’s profiles for generating useful recommendations. Privacy sensitive data is used for to collect the data. Collaborative filtering technique gives privacy for sensitive data if data is misused by other service providers or leaked. Existing system uses Paillier encryption algorithm & DGK algorithm to secure user data from malicious third party as well as to protect the private data against service provider but system is more complex and inefficient. Proposed system protects the privacy of user using encrypting the sensitive data. The system uses multiple homomorphic algorithms to secure user data from service providers. The system is used to protect the confidential data of user against the service provider while providing online services. Encrypting private data is recommended and process on data to generate recommendations. To construct efficient system that does not require the active participation of the user. The experiment shows that the result that provide the security by hiding the personal data of user from third party DOI: 10.17762/ijritcc2321-8169.15076

Towards Private Biometric Authentication and Identification

Handwriting and speech are important parts of our everyday lives. Handwriting recognition is the task that allows the recognizing of written text, whether it be letters, words or equations, from given data. When analyzing handwriting, we can analyze static images or the recording of written text through sensors. Handwriting recognition algorithms can be used in many applications, including signature verification, electronic document processing, as well as e-security and e-health related tasks. The OnHW datasets consists of a set of datasets which, through the use of various sensors, captures the writing of characters, words, symbols and equations, recorded in the form of multivariate time series. We begin by developing character recognition models, targeting letters (and later symbols), trained and tested using the OnHW-chars dataset (and later the split OnHW-equations dataset). Our models were able to improve upon the accuracy of the previous best results on both datasets explored. Using our machine learning (ML) models, we provide 11.3%-23.56% improvements over the previous best ML models. Using deep learning (DL), as well as ensemble techniques, we were able to improve on the best previous models by 3.08%-7.01%. In addition to the accuracy improvements, we aim to provide some level of explainability, using a specialized version of LIME for time series data. This explanation helps provide some rationale for why the models make sense for the data, as well as why ensemble methods may be useful to improve accuracy rates for this task. To verify the robustness of our models trained over the OnHW-chars dataset, we trained our DL models using the same model parameters over a more recently published OnHW-equations dataset. Our DL models with ensemble learning provide 0.05%-4.75% improvements over the previous best DL models. While the character recognition task has many applications, when using it to provide a service, it is important to consider user privacy since handwriting is biometric data and contains private information. Next, we design a framework that uses multiparty computation (MPC) to provide users with privacy over their handwritten data, when providing a service for character recognition. We then implement the framework using the models trained on public data to provide private inference on hidden user data. This framework is implemented in the CrypTen MPC framework. We obtain results on the accuracy difference of the models when making inference using MPC, as well as the costs associated with performing this inference. We found a 0.55%-1.42% accuracy difference between plaintext inference and inference with MPC. Next, we pivot to explore writer identification, which involves identifying the writer of some handwritten text. We use the OnHW-equations dataset for our analysis, which at the time of writing has not been used for this task before. We first analyze and reformat the data to fit the writer identification task, as well as remove bias. Using DL models, we obtain accuracy results of up to 91.57% in identifying the writer using their handwriting. As with private inference in the character recognition task, it is important to account for user privacy when training writer identification models and making inference. We design and implement a framework for private training and inference for the writer recognition task, using the CrypTen MPC framework. Since training these models is very costly, we use simpler CNN's for private writer recognition. The chosen CNN trained privately in MPC obtained an accuracy of 77.45%. Next, we analyze the costs associated with privately training the CNN and other CNN's with altered model architectures. Finally, we switch to explore voice as a biometric in the speaker verification task. As with handwriting, a person's voice contains unique characteristics which can be used to determine the speaker. Not only can voice be analyzed similarly with handwriting, in that we can explore the speech recognition and speaker identification tasks, it comes with similar privacy risks for users. We design and implement a unique framework for private speaker verification using the MP-SPDZ MPC framework. We analyze the costs associated with training the model and making inferences, with our main goal being to determine the time it takes to make private inference. We then used these times as part of a survey conducted to determine how much people value the privacy of their biometrics and how long they were willing to wait for the increased privacy. We found that people were willing to tolerate significant time delays in order to privately authenticate themselves, when primed with the benefits of using MPC for privacy

Maturity and Performance of Programmable Secure Computation

Secure computation research has gained traction internationally in the last five years. In the United States, the DARPA PROCEED program (2011-2015) focused on development of multiple SC paradigms and improving their performance. In the European Union, the PRACTICE program (2013-2016) focuses on its use to secure cloud computing. Both programs have demonstrated exceptional prototypes and performance improvements. In this paper, we collect the results from both programs and other published literature to present the state of the art in what can be achieved with today\u27s secure computing technology. We consider linear secret sharing based computations, garbled circuits and fully homomorphic encryption. We describe theoretical and practical criteria that can be used to characterize secure computation paradigms and provide an overview of common benchmarks such as AES evaluation

A Survey on Differential Privacy with Machine Learning and Future Outlook

Nowadays, machine learning models and applications have become increasingly pervasive. With this rapid increase in the development and employment of machine learning models, a concern regarding privacy has risen. Thus, there is a legitimate need to protect the data from leaking and from any attacks. One of the strongest and most prevalent privacy models that can be used to protect machine learning models from any attacks and vulnerabilities is differential privacy (DP). DP is strict and rigid definition of privacy, where it can guarantee that an adversary is not capable to reliably predict if a specific participant is included in the dataset or not. It works by injecting a noise to the data whether to the inputs, the outputs, the ground truth labels, the objective functions, or even to the gradients to alleviate the privacy issue and protect the data. To this end, this survey paper presents different differentially private machine learning algorithms categorized into two main categories (traditional machine learning models vs. deep learning models). Moreover, future research directions for differential privacy with machine learning algorithms are outlined.Comment: 12 pages, 3 figure

Behavioral types in programming languages

A recent trend in programming language research is to use behav- ioral type theory to ensure various correctness properties of large- scale, communication-intensive systems. Behavioral types encompass concepts such as interfaces, communication protocols, contracts, and choreography. The successful application of behavioral types requires a solid understanding of several practical aspects, from their represen- tation in a concrete programming language, to their integration with other programming constructs such as methods and functions, to de- sign and monitoring methodologies that take behaviors into account. This survey provides an overview of the state of the art of these aspects, which we summarize as the pragmatics of behavioral types