Definition Of Privacy Research Articles

Privacy Profiles and Amplification by Subsampling

Differential privacy provides a robust quantifiable methodology to measure and control the privacy leakage of data analysis algorithms.A fundamental insight is that by forcing algorithms to be randomized, their privacy leakage can be characterized by measuring the dissimilarity between output distributions produced by applying the algorithm to pairs datasets differing in one individual.After the introduction of differential privacy, several variants of the original definition have been proposed by changing the measure of dissimilarity between distributions, including concentrated, zero-concentrated and R{\'e}nyi differential privacy.
 The first contribution of this paper is to introduce the notion of privacy profile of a mechanism.This profile captures all valid $(\varepsilon,\delta)$ differential privacy parameters satisfied by a given mechanism, and contrasts with the usual approach of providing guarantees in terms of a single point in this curve.We show that knowledge of this curve is equivalent to knowledge of the privacy guarantees with respect to the alternative definitions listed above.This sheds further light into the connections between different privacy definitions, and suggests that these should be considered alternative but otherwise equivalent points of view.
 The second contribution of this paper is to apply the privacy profiles machinery to study the so-called ``privacy amplification by subsampling'' principle, which ensures that a differentially private mechanism run on a random subsample of a population provides higher privacy guarantees than when run on the entire population.Several instances of this principle have been studied for different random subsampling methods, each with an ad-hoc analysis. In this paper we set out to study this phenomenon in detail with the aim to provide a general method capable of recovering prior analyses in a streamlined fashion.Our method makes extensive use of coupling argument, and introduces a new tool to analyse differential privacy for mixture distributions.

2016-da məlumat skandalının etik təhlili: Cambridge Analytica və Facebook

This paper analyzes the ethics behind the actions of the 2016 Data Scandal on the example of 2 major sides, Facebook and Cambridge Analytica. Subsequent events such as bankruptcy of Cambridge Analytica and a significant drop in the stock prices of Facebook (a fall of 24%, equivalent to $134 billion.) were an integral part of this research paper to explore the role of the attitudes of the business entities over bankruptcy in these kinds of scandals. Thereby, a comparison technique has been employed to analyze the ethical dimension of the bankruptcy of Cambridge Analytica, and how the attitude of Facebook provided a chance of survival and recovery within this process. The outcome of the research clearly identifies that even in the corporative entities bypassing or violating the ethical standards can be observed. Albeit, there is a strong correlation between the degree of ethical standards and the sustainability of the businesses from the aspect of customers, partners, and the government. The article continued with the analysis of the significance of the immediate implementation of the ethical standards and deterrent defense with a manner of “bona fide” in these types of scandals to handle the crisis. The research concluded with an ethical analysis of data analysis and data mining from the Kantian definition of autonomy, Jurgen Habermas’s definition of privacy in the era of digitalization.

Secure Metric Learning via Differential Pairwise Privacy

Distance Metric Learning (DML) has drawn much attention over the last two decades. A number of previous works have shown that it performs well in measuring the similarities of individuals given a set of correctly labeled pairwise data by domain experts. These important and precisely-labeled pairwise data are often highly sensitive in real world (e.g., patients similarity). This paper studies, for the first time, how pairwise information can be leaked to attackers during distance metric learning, and develops differential pairwise privacy (DPP), generalizing the definition of standard differential privacy, for secure metric learning. Unlike traditional differential privacy which only applies to independent samples, thus cannot be used for pairwise data, DPP successfully deals with this problem by reformulating the worst case. Specifically, given the pairwise data, we reveal all the involved correlations among pairs in the constructed undirected graph. DPP is then formalized that defines what kind of DML algorithm is private to preserve pairwise data. After that, a case study employing the contrastive loss is exhibited to clarify the details of implementing a DPP- DML algorithm. Particularly, the sensitivity reduction technique is proposed to enhance the utility of the output distance metric. Experiments both on a toy dataset and benchmarks demonstrate that the proposed scheme achieves pairwise data privacy without compromising the output performance much (Accuracy declines less than 0.01 throughout all benchmark datasets when the privacy budget is set at 4).

A logical characterization of differential privacy

Differential privacy is a formal definition of privacy ensuring that sensitive information relative to individuals cannot be inferred by querying a database. In this paper, we exploit a modeling of this framework via labeled Markov Chains (LMCs) to provide a logical characterization of differential privacy : we consider a probabilistic variant of the Hennessy-Milner logic and we define a syntactic distance on formulae in it measuring their syntactic disparities. Then, we define a trace distance on LMCs in terms of the syntactic distance between the sets of formulae satisfied by them. We prove that such distance corresponds to the level of privacy of the LMCs. Moreover, we use the distance on formulae to define a real-valued semantics for them, from which we obtain a logical characterization of weak anonymity : the level of anonymity is measured in terms of the formulae distinguishing the considered LMCs. Then, we focus on bisimulation semantics on nondeterministic probabilistic processes and we provide a logical characterization of generalized bisimulation metrics , namely those defined via the generalized Kantorovich lifting . Our characterization is based on the notion of mimicking formula of a process and the syntactic distance on formulae, where the former captures the observable behavior of the corresponding process and allows us to characterize bisimilarity. We show that the generalized bisimulation distance on processes is equal to the syntactic distance on their mimicking formulae. Moreover, we use the distance on mimicking formulae to obtain bounds on differential privacy. • Logical characterization of differential privacy. • Novel characterization technique based on metric over modal formulae. • Logical characterization of generalized bisimulation metrics. • Definition of real-valued semantics of formulae via metric on formulae. • Logical characterization of weak anonymity.

Respecting privacy in care services

Purpose In care homes concerns about abuse have established a culture where all information pertaining to a person must be shared, and little attention is paid to privacy in its broader sense. The purpose of this paper is to take a human rights perspective and consider how information governance may impact on the health, well-being and quality of life of residents. It proposes a proactive approach and presents a template for a privacy impact assessment which services could use to improve their approach to privacy, protecting the human rights of those in their care, contributing to their independence and improving outcomes. Design/methodology/approach A review of historical and current thinking about the value of privacy in human services and wider society leads to a series of challenges to the way in which privacy is upheld in residential care services. Findings Recent preoccupations with data privacy have led to a myopic neglect of broader considerations of privacy. Whilst it continues to be important to protect the confidentiality of personal data and to ensure that residents are protected from abuse, human services that provide 24 hour care in congregated settings must not neglect broader components of privacy. Research limitations/implications Privacy impact assessments have been widely used to check whether data privacy is being upheld. The broader concept that might be termed “Big Privacy” is introduced within which data privacy is but one section. It is suggested that big privacy is severely compromised in residential care settings, thus denying residents their human right to privacy. The extent of such violation of rights should be investigated. Practical implications Having set out the potential reach of the human right to privacy, important work needs to be done to find out how privacy might be upheld in the real world of congregate residential care. Some service providers may have solutions to the organisational challenges, have addressed staff training needs and revised risk assessment strategies so that privacy is upheld alongside other rights. Social implications Nearly half a million people live in congregate residential care settings in England, and deprivation of privacy is argued to be a significant deprivation of human rights. Occasional tragedies and scandals in congregate settings create pressure for increasing the level of surveillance, and the right to privacy is sacrificed. This paper offers a challenge to this process, arguing that competing rights need to be balanced and privacy is an essential component of a decent quality of life. Originality/value Personal growth and development depends to some extent on choice and control over access to privacy. Recent changes in the law regarding data protection have narrowed our thinking about privacy until it is a small concept, largely concerned with data handling. This paper invites consideration of big privacy, and invites congregate residential care settings to consider how a deep and broad definition of privacy could transform these services.

Privacy at Work: A Review and a Research Agenda for a Contested Terrain

Privacy in the workplace is a pivotal concern for employees and employers. Employees expect to be in control of the personal information and access they provide to the organization. Employers, however, expect extensive information regarding their employees as well as extensive access to employees’ presence. The chasm between these two often competing expectations has been magnified by regulatory and technological trends. We begin the review by integrating viewpoints from multiple disciplines to disentangle definitions of privacy and to delineate the privacy contexts of information privacy and work environment privacy. We then identify the key stakeholders of privacy in the workplace and describe their interests. This discussion serves as a platform for our stakeholders’ privacy calculus model, which in turn provides a framework within which we review empirical findings on workplace privacy from organizational research and related disciplines and from which we identify gaps in the existing research. We then advance an extensive research agenda. Finally, we draw attention to emerging technologies and laws that have far-reaching implications for employees and employers. Our review provides a road map for researchers and practitioners to navigate the contested terrain of workplace privacy.

Privacy Protection for Recommendation System: A Survey

Recommendation system have become one of the most well-liked and accepted way to solve overload of information or merchandise. By collecting user’s personal data for processing, suitable lists of information or merchandise are provided to the potential consumers. For online business, recommendation systems have become an extremely effective revenue driver and developed rapidly. Although recommendation systems are great beneficial, directly exposing privacy data to the recommender may lead to leakage of privacy and cause risks. Therefore, quality of recommendation and privacy protection are both important metrics in recommendation. In this paper we present a review investigating development in recommendation systems with privacy protection, including the definition of privacy, classification of privacy leakage, taxonomy of privacy, measuring of privacy risk, policies for privacy protection, approaches of privacy protection and models of privacy protection. We also speculate on the future direction.

Secure and Privacy-Preserving Consensus

Consensus is fundamental for distributed systems since it underpins key functionalities of such systems ranging from distributed information fusion, decision making, to decentralized control. In order to reach an agreement, existing consensus algorithms require each agent to exchange explicit state information with its neighbors. This leads to the disclosure of private state information, which is undesirable in cases where privacy is of concern. In this paper, we propose a novel approach for undirected networks, which can enable secure and privacy-preserving average consensus in a decentralized architecture in the absence of an aggregator or third party. By leveraging partial homomorphic cryptography to embed secrecy in pairwise interaction dynamics, our approach can guarantee convergence to the consensus value (subject to a quantization error) in a deterministic manner without disclosing a node's state to its neighbors. We provide a new privacy definition for dynamical systems, and give a new framework to rigorously prove that a node's privacy can be protected as long as it has at least one legitimate neighbor, which follows the consensus protocol faithfully without attempts to infer other nodes’ states. In addition to enabling resilience to passive attackers aiming to steal state information, the approach also allows easy incorporation of defending mechanisms against active attackers who try to alter the content of exchanged messages. Furthermore, in contrast to existing noise-injection-based privacy-preserving mechanisms that have to reconfigure the entire network when the topology or number of nodes varies, our approach is applicable to dynamic environments with time-varying coupling topologies. This secure and privacy-preserving approach is also applicable to weighted average consensus as well as maximum/minimum consensus under a new update rule. Numerical simulations and comparison with existing approaches confirm the theoretical results. Experimental results on a Raspberry-Pi board based microcontroller network are also presented to verify the effectiveness and efficiency of the approach.

Impact of Prior Knowledge and Data Correlation on Privacy Leakage: A Unified Analysis

It has been widely understood that differential privacy (DP) can guarantee rigorous privacy against adversaries with arbitrary prior knowledge. However, recent studies demonstrate that this may not be true for correlated data, and indicate that three factors could influence privacy leakage: the data correlation pattern, prior knowledge of adversaries, and sensitivity of the query function. This poses a fundamental problem: what is the mathematical relationship between the three factors and privacy leakage? In this paper, we present a unified analysis of this problem. A new privacy definition, named \textit{prior differential privacy (PDP)}, is proposed to evaluate privacy leakage considering the exact prior knowledge possessed by the adversary. We use two models, the weighted hierarchical graph (WHG) and the multivariate Gaussian model to analyze discrete and continuous data, respectively. We demonstrate that positive, negative, and hybrid correlations have distinct impacts on privacy leakage. Considering general correlations, a closed-form expression of privacy leakage is derived for continuous data, and a chain rule is presented for discrete data. Our results are valid for general linear queries, including count, sum, mean, and histogram. Numerical experiments are presented to verify our theoretical analysis.

Understanding Game-Based Privacy Proofs for Energy Consumption Aggregation Protocols

Despite the large number of privacy-preserving aggregation protocols in the smart grid, there is no common methodology for evaluating and comparing their privacy guarantees. Protocol discussion often lacks a formal evaluation of the proposed privacy guarantees. In order to transfer the well-established formal methodology of game-based proofs to the smart grid domain, in this paper we: 1) present a game-based privacy definition which addresses the privacy requirement to be captured in an aggregation protocol (the definition may be used or extended for other protocols); 2) exemplify our game-based proof technique for two aggregation protocols; and 3) provide a novel and compact way to visualize and easily compare the privacy guarantees of different protocols. We employ two sample protocols that reflect the basis of the most common approaches currently found in the energy aggregation literature. In summary, we contribute a guideline on how to conduct formal evaluations for protocol developers as well as an easy-to-understand way to assess the privacy guarantees of different aggregation protocols for non-experts.

Decision Tree Classification with Differential Privacy

Data mining information about people is becoming increasingly important in the data-driven society of the 21st century. Unfortunately, sometimes there are real-world considerations that conflict with the goals of data mining; sometimes the privacy of the people being data mined needs to be considered. This necessitates that the output of data mining algorithms be modified to preserve privacy while simultaneously not ruining the predictive power of the outputted model. Differential privacy is a strong, enforceable definition of privacy that can be used in data mining algorithms, guaranteeing that nothing will be learned about the people in the data that could not already be discovered without their participation. In this survey, we focus on one particular data mining algorithm—decision trees—and how differential privacy interacts with each of the components that constitute decision tree algorithms. We analyze both greedy and random decision trees, and the conflicts that arise when trying to balance privacy requirements with the accuracy of the model.

TRACING THE EVOLVING FACTS OF, A HUMAN RIGHT TO PRIVACY WITH INDIAN PERSPECTIVE

Human Rights are basic rights and freedoms that belong to every person in world from birth until death. They apply of where you are from, what you believe or how you choose to live your life. Privacy is a concomitant of the right of an individual to exercise over his or her personality. There are certain rights which are natural to or inherent in human right. The human element in life is impossible to conceive without the existence of natural rights. It is very difficult to give precise definition of privacy; with the changing time the definition of privacy and its aspect has also changed. Article 12 of Universal Declaration of Human Rights recognize about the right to privacy. Article 51 of the Indian constitution requires the state to endeavor to foster respect for international law and treaty obligation in the dealings of organized people with one another. The present paper focuses on changing dimension of privacy law from the various judgments given by the judiciary, how the aspect of privacy has evolved from early cases till the day when right to privacy, a human right has been declared as fundamental right by Indian judiciary system.

Analysis and Reflections on the Right to Privacy and Intimity, Your Need Inclusion and Definition in the Political Constitution of the United Mexican States

A fundamental point that occupies us in the present analysis is that contents are framed within the dimensions of the right to intimacy, and to privacy since in this respect there is no clear definition in the Political Constitution of the United Mexican States and the Supreme Court of Justice of the Nation does not clarify both terms either. The improvements in the quality of life that have been reported and the new ways of promoting the dignity of people have found in communication technologies new ways of attacking personal rights by those who use these new media for illicit purposes. They are more difficult attacks to investigate, prosecute and punish given the current technological characteristics and that have generated new phenomena that disrupt the intimacy and privacy of people. Therefore, one of the central themes of this discussion is in the proper definition and limits of intimacy and privacy. Both are fundamental and indispensable rights in the development of the people embodied in the Political Constitution of the United Mexican States. Consequently, it is necessary that these two rights are clearly recognized and defined in the Mexican Constitution and a secondary legislation is created that regulates its limits in a timely manner, considering that intimacy is the reserved or more particular part of the thoughts, affections or issues the interior of a person, which appears in a restrictive sphere that belongs to the most personal nucleus of the individual, while privacy is the right that people have to maintain and confidentially reserve files; records, personal or public databases, but also, privacy would depend on the cultural, political and social context in which one lives, that is, there are countries that are characterized by authoritarian governments with strong restrictions in many personal areas, such as free access to the Internet, freedom of the press, freedom of assembly, free formation of social organizations and freedom of worship.

Privacy and Social Media: Defining Privacy in the Usage of Path

The existence of social media has changed the landscape of human’s relationship. Through social media, people are able to present many versions of themselves in many platforms. In this era of polymediation of the self, the discussion regarding to privacy becomes arguable, moreover, with the presence of Path; a social media platform which presents itself as a private social media. Hence, in the sociocultural context of Indonesian society, it is important to see how the definition of privacy is constructed by the existence of Path. Therefore, this research is conducted in order to analyze and explore how privacy is perceived by the social media users nowadays, particularly the users of Path, and why they perceive it in that certain way. This research is conducted with ethnography as the main method and virtual ethnography as the supporting method. From the research, it is found that users’ way of defining privacy is embodied in two levels: online self-presentation and personal space construction. In the first level, the stages of privacy offered by Path have created the fragmented-self among users. This fragmentation has resulted in “the ambivalent self”, “self that desires recognition”, and “self that searches for freedom”. In the second level, the mediality of Path has served the users of the ability to construct their own personal space in social media space. This construction of the personal space has resulted in “space of comfort in similarity”, “space of pseudo-liberation”, and “space that demolishes the panoptic”. Henceforth, these findings lead to a conclusion that usage practices of social media has killed the authentic self and created a personal space that gives the sense of the absence of control, hierarchy, and social surveillance. Eventually, privacy for Path is defined by the process of exchange of “the self and personal information” with “social recognition, sense of equality, and reciprocal relationship”.

DPSAaS

Differential privacy has emerged as the de facto standard for privacy definitions, and been used by, e.g ., Apple, Google, Uber, and Microsoft, to collect sensitive information about users and to build privacy-preserving analytics engines. However, most of such advanced privacy-protection techniques are not accessible to mid-size companies and app developers in the cloud. We demonstrate a lightweight middleware DPSAaS , which provides <u>d</u> ifferentially <u>p</u> rivate data-<u>s</u>haring-and-<u>a</u>nalytics functionality <u>a</u> s cloud <u>s</u>ervices. We focus on multi-dimensional analytical (MDA) queries under local differential privacy (LDP) in this demo. MDA queries against a fact table have predicates on (categorical or ordinal) dimensions and aggregate one or more measures. In the absence of a trusted agent, sensitive dimensions and measures are encoded in a privacy-preserving way locally using our LDP data sharing service, before being sent to the data collector. The data collector estimates the answers to MDA queries from the encoded data, using our data analytics service. We will highlight the design decisions of DPSAaS and twists made to LDA algorithms to fit the design, in order to smoothly connect DPSAaS to the data processing platform and analytics engines, and to facilitate efficient large-scale processing.

PrivateSQL

Differential privacy is considered a de facto standard for private data analysis. However, the definition and much of the supporting literature applies to flat tables. While there exist variants of the definition and specialized algorithms for specific types of relational data (e.g. graphs), there isn't a general privacy definition for multi-relational schemas with constraints, and no system that permits accurate differentially private answering of SQL queries while imposing a fixed privacy budget across all queries posed by the analyst. This work presents PrivateSQL, a first-of-its-kind end-to-end differentially private relational database system. PrivateSQL allows an analyst to query data stored in a standard database management system using a rich class of SQL counting queries. PrivateSQL adopts a novel generalization of differential privacy to multi-relational data that takes into account constraints in the schema like foreign keys, and allows the data owner to flexibly specify entities in the schema that need privacy. PrivateSQL ensures a fixed privacy loss across all the queries posed by the analyst by answering queries on private synopses generated from several views over the base relation that are tuned to have low error on a representative query workload. We experimentally evaluate PrivateSQL on a real-world dataset and a workload of more than 3, 600 queries. We show that for 50% of the queries PrivateSQL offers at least 1, 000x better error rates than solutions adapted from prior work.

Local differential privacy for social network publishing

Social networks often contain sensitive information. Releasing social network data could possibly seriously jeopardize individual privacy. Therefore, we need to protect privacy when publish social network data. However, the current differential privacy for social network data publishing seriously influences the structure of the social network. We propose a local differential privacy model for social network publishing that preserves community structure information. The model generates the synthetic social network data as published versions under the structural constraints of the edge probability reconstruction. We theoretically prove that the local differential privacy model satisfies the definition of differential privacy. We evaluate the efficacy of the proposed method using three real-life social network datasets and show that our method effectively preserves network structural properties, while ensuring a strong degree of privacy.

Privacy stochastic games in distributed constraint reasoning

In this work, we approach the issue of privacy in distributed constraint reasoning by studying how agents compromise solution quality for preserving privacy, using utility and game theory. We propose a utilitarian definition of privacy in the context of distributed constraint reasoning, detail its different implications, and present a model and solvers, as well as their properties. We then show how important steps in a distributed constraint optimization with privacy requirements can be modeled as a planning problem, and more specifically as a stochastic game. We present experiments validating the interest of our approach, according to several criteria.

Cardinality Estimators do not Preserve Privacy

Abstract Cardinality estimators like HyperLogLog are sketching algorithms that estimate the number of distinct elements in a large multiset. Their use in privacy-sensitive contexts raises the question of whether they leak private information. In particular, can they provide any privacy guarantees while preserving their strong aggregation properties? We formulate an abstract notion of cardinality estimators, that captures this aggregation requirement: one can merge sketches without losing precision. We propose an attacker model and a corresponding privacy definition, strictly weaker than differential privacy: we assume that the attacker has no prior knowledge of the data. We then show that if a cardinality estimator satisfies this definition, then it cannot have a reasonable level of accuracy. We prove similar results for weaker versions of our definition, a nd a nalyze t he p rivacy o f existing algorithms, showing that their average privacy loss is significant, e ven f or m ultisets w ith l arge cardinalities. We conclude that strong aggregation requirements are incompatible with any reasonable definition o f privacy, and that cardinality estimators should be considered as sensitive as raw data. We also propose risk mitigation strategies for their real-world applications.

We Care About Different Things: Non-Elite Conceptualizations of Social Media Privacy

This study explores privacy from the perspective of the user. It leverages a “framing in thought” approach to capture how users make sense of privacy in their social media use. It builds on a unique dataset of privacy definitions collected from a representative sample of 608 US social media users. The data are analyzed using topic modeling and semantic network analysis to unpack the multidimensionality of social media privacy. These dimensions are further examined in relation to established demographic antecedents of privacy concerns and behaviors. Results indicate the dominance of frames related to horizontal dimensions of privacy, or privacy vis-à-vis peers, as compared with the vertical dimensions, or privacy vis-à-vis institutions. In addition, the findings suggest that user conceptualization of privacy reflects a cognate-based approach that emphasizes control and limits to information access. Implications for privacy research, policy, and technology design are discussed.